literature review on covid 19 impact

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Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

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• Published: 25 April 2023
• Volume 51 , pages 1907–1961, ( 2024 )

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• Kwang-Sub Lee   ORCID: orcid.org/0000-0001-9565-2252 1 &
• Jin Ki Eom 1  

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The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19’s spread is inevitably associated with human mobility. Given the pandemic’s severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic’s impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport’s response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

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Introduction

The novel coronavirus outbreak (COVID-19), was first reported from Wuhan, China on December 31, 2019 (Gkiotsalitis and Cats, 2021 ; De Vos 2020 ). The World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020, because the highly contagious disease had rapidly spread, affecting people’s lives worldwide (Mashrur et al. 2022 ; Zhang et al. 2020b ; De Haas et al. 2020 ; Hadjidemetriou et al. 2020 ). The current crisis differs from previous epidemic trends (i.e., SARS or H1N1) in that it is global, difficult to contain, has a rapid spread rate, and a high death toll (Borkowski et al. 2021 ). Given the pandemic’s severity, it is a crucial that governments control the spread. Therefore, they implemented a combination of measures, applying various approaches to isolate outbreaks and avoid further exposures by reducing close contact with the virus (Jaekel and Muley 2022 ; Wang et al. 2022 ; Arimura et al. 2020 ; Lau et al. 2020 ). These countermeasures included forced or recommended measures, such as city lockdowns, confinement, halting domestic and international flights, travel restrictions, workplace closures, and social distancing (Chen et al. 2022a ; Lu et al. 2021 ; Pan et al. 2020 ; Chinazzi et al. 2020 ; Shakibaei et al. 2020 ). However, the pandemic is still not fully under control and its impacts persist as of March 2023, including a huge death toll and negative influences on quality of life, such as economic losses, business closures, and social activities (Kim 2021 ; Tan and Ma 2021 ).

According to WHO, COVID-19 is respiratory and spreads mainly through contact with an infected person (WHO, 2021 ; Moslem et al. 2020 ). Spread is inevitably associated with human movement, and the transport sector plays an important role in reducing the spread of infection (Rothengatter et al. 2021 ; Liu et al., 2020a ; Moslem et al. 2020 ; Sokadjo and Atchade 2020 ; Lee et al. 2020a ; Muley et al. 2020 ). Because there is a strong correlation between infectious diseases and movement of people, many researchers argued that travel restrictions could contribute to limiting the virus (Chen et al. 2020 , 2022a ; Choi et al. 2022 ; Zhang et al. 2021b ; Fatmi 2020 ; Liu et al. 2020b ; Muley et al. 2020 ). For instance, several studies showed that population emigration from Wuhan was highly correlated to imported cases in other Chinese cities Su et al. 2022a ; Liu et al. 2020b ; Chen et al. 2020 ; Zhao et al., 2020a ; Shi and Fang 2020 ; Liu et al., 2020a ), and that lockdown policies effectively slow COVID-19 spread (Gramsch et al. 2022 ; Mars et al. 2022 ; Chen et al. 2022a ; Li et al. 2021a ; Wen et al. 2021 ; Anzai et al. 2020 ; Aloi et al. 2020 ; Cintia et al. 2020 ; De Haas et al. 2020 ).

Given the high transmissibility, limited epidemiological understanding, and lack of a specific COVID-19 treatment, understanding human mobility and containment measure effects is crucial to mitigating COVID-19’s impact (Gramsch et al. 2022 ; Liu et al. 2022 ; Ciuffini et al. 2021 ; Hadjidemetriou et al. 2020 ; Cintia et al. 2020 ; Muley et al. 2020 ) reviewed previous transport and infectious disease literature, including COVID-19, and found that the transport sector has a two-fold role during an infectious disease outbreak: controlling infection spread and assessing the impact of reduced outdoor activities on the transport sector. With different countries’ rapidly changing environments, it is extremely difficult to quantify the magnitude of mobility related measures’ impact and draw a general and consistent conclusion (Tan and Ma 2021 ). Although vaccine is now available, some moderate regulations, such as social distancing and personal protective measures, might remain for a long time to mitigate the pandemic and to prepare for another pandemic wave. Given that the COVID-19 pandemic differed from previous epidemic trends, COVID-19 research may not be directly applicable to future epidemics. However, understanding changes to travel behavior characteristics during COVID-19, and examining factors affecting travel patterns and various preventive measures’ effectiveness, provides important information for policy makers.

A few literature reviews have been published so far, but their topics are limited to a specific transportation field (e.g., the impacts of COVID-19 on public transport by Gkiotsalitis and Cats, 2021 ) and a specific subject (e.g., transportation policies and mitigation strategies by Peralvo et al. 2022 ; and the built environment and human factors by Alidadi and Sharifi 2022 ), or to review in a particular way (e.g., bibliometric analysis by Benita 2021 ). There is one study similar to our intention that Muley et al. ( 2020 ) systematically discussed the impact of COVID-19 on the transport sector. However, they did not consider studies on the effects of various government policies in response to COVID-19. In particular, they reviewed the subjects of studies up to June 2020, and since many COVID-19-related papers are constantly being published, it is necessary to update on the latest research. Accordingly, our study’s key objective is to synthesize evidence from the scientific literature and case studies (published up to March 2023) on the impact of COVID-19 on urban transportation, to assist policy makers and urban and transportation planners better prepare for future health emergencies. We review studies on COVID-19’s impact on human mobility and the corresponding governments’ countermeasures to present a comprehensive synthesis of previous studies with diverse perspectives, and to discuss future research needs. We conduct thorough literature reviews, identify and classify the material by subject, and present key results and controversial issues.

The specific topics covered in this study and the structure of the paper are as follows. Chapter 2 describes the methodology (e.g., literature review strategy and selection criteria) of this study. Chapters 3 and 4 review studies on COVID-19 and government measures and their impact on the transport sector. In fact, changes in travel patterns related to the pandemic may have various causes, such as government measures to limit the spread of the virus, people’s compliance with such measures, and changes in activities and travel behavior that each person has selectively taken to avoid contagion. However, it seems difficult to distinguish the exact cause and effect. Accordingly, we distinguish between studies that do not analyze the effectiveness of government policies (Chap. 3) and studies that explicitly analyze the effectiveness (Chap. 4). In Chap. 3, literatures on the overall impacts of the COVID-19 outbreak on mobility regardless of the presence of the government’s specific measures are also divided into: (1) studies on mobility changes quantitatively analyzed based on observed transportation data including mobile phone data and Google Mobility reports, and (2) survey-based studies to investigate changes in personal travel behavior according to demographic and socioeconomic characteristics. The first topic is further subdivided by the relationship between COVID-19 and human mobility (Sect.  3.1 ) and impacts on overall mobility (Sect.  3.2 ), public transportation (Sect.  3.3 ), and other impacts (Sect.  3.4 ). Section  3.5 focuses on changes in personal travel and activity behavior based on surveys. Chapter 4 reviews studies that explicitly analyzed how the government’s specific measures to contain the spread of COVID-19 affect mobility and whether reduced traffic effectively reduces the spread of infection. Specifically, we review studies on travel restriction policies’ impacts on reducing human mobility (Sect.  4.1 ), the relationship between travel restriction policy and COVID-19 transmission (Sect.  4.2 ), and conflicting findings and issues on travel restriction policy effects (Sect.  4.3 ). Chapter 5 discusses what future research topics are needed and then concludes in Chap. 6.

Methodology

Literature review strategy.

Given the unprecedented severity of COVID-19 and the extent of spread, many studies have been published within a short time frame. The review was conducted through five steps (Snyder 2019 ; Wolfswinkel et al., 2013 ; Khan et al. 2003 ): (1) developing research questions; (2) searching for relevant studies based on inclusion and exclusion criteria; (3) assessing studies’ quality to identify literature relevant to our research interests; (4) identifying research topics and classifying them by the subject; and (5) summarizing and synthesizing the selected studies’ results.

Literature selection: inclusion and exclusion criteria

After the systematic review objective was defined, we conducted a literature search using search engines, including Google Scholar and three of the most recognized academic databases that cover top-notch journals, Scopus, Science Direct, and Web of Science. The search was restricted to journal articles that included selected keywords. We used comprehensive combinations of search terms; additionally, two main search strings were employed and combined using parentheses with “AND,” one specifying all the relevant keywords for “COVID-19” and “transport,” the other specifying keywords such as “mobility,” “impact,” “effect,” “travel behavior,” “restriction,” and “measurement.”

After relevant literature was initially identified based on titles, abstracts, and conclusions, we applied the following inclusion criteria to narrow the results: (1) studies published from 2020 to the present (March 2023); (2) studies with access to the full text written in English; (3) all review articles, empirical studies, conferences or proceedings, peer-reviewed journals, including quantitative or qualitative case studies; and (4) studies examining all types of human mobility, including private car, public transport (bus, railway), bicycle, and personal mobility. However, although impacts related to the aviation sector were not specifically excluded, the analysis focused on intra-country mobility as much as possible, because inter-country movement is directly affected by each country’s immigration policy. From the selected studies, we screened article relevance using the following exclusion criteria: (1) studies covering COVID-19 in general and not related to the transport sector (i.e., COVID-19 impacts not related to human mobility and activity); (2) studies that focused on personal health, pharmacological intervention, epidemiological or pathological evidence; and (3) studies on freight/marine/water transport.

Literature extraction

Before the full text assessment, we also reviewed reference lists for relevant literature and discovered additional relevant articles through forward and backward reference tracing, adding them to the search lists to complement the literature identified through database searches. Subsequently, duplicates were removed, and the remaining studies were further screened for relevance and scope by examining each article’s abstract, introduction, and conclusion. After filtering, 364 articles remained for the final analysis. We thoroughly reviewed each study’s content and conducted thematic analyses to categorize studies based on their topics and study perspectives. This approach effectively identified each study’s purpose, data, and results, and grouped them into major topics and sub-topics. When the study subject and transport means were similar across multiple studies, those not significantly meaningful to this study’s review subject were not included in the analysis. The articles relevant to each subject were extracted and summarized in tables that included publication details (author(s) and year), study area, research objective and method, data type, and transport mode type.

Overall impacts of the pandemic on mobility regardless of Government’s measures

This chapter reviews the literatures on the overall impacts of the COVID-19 outbreak on mobility regardless of the presence or absence of the government’s specific measures. Specifically, existing studies are discussed under the following topics: (1) the relationship between human mobility and COVID-19 transmission; (2) the impact of COVID-19 on overall mobility based on observed data; (3) the impact on public transportation; (4) other impacts; and (5) changes in personal travel behavior based on survey data.

Relationship between human mobility and COVID-19 transmission

Increased traffic volume increases the possibility of contact between people and thus increases potential COVID-19 spread. Extensive research investigated the link between human mobility and COVID-19 spread, and showed a positive correlation (Table  1 ). Because COVID-19 was first detected in Wuhan, China, many studies focused on data from China Shi and Fang 2020 ; Chen et al. 2020 ; Liu et al. 2020b ; Zhao et al., 2020a ). For example, Shi and Fang ( 2020 ) investigated the temporal relationship between daily outbound traffic from Wuhan to 31 Chinese provinces and COVID-19 incidence during the virus’s early spread in 2020. Outbound traffic volume was positively associated with COVID-19 incidence in all provinces, with correlation coefficients ranging from 0.22 to 0.78, and statistically significant at the 95% level. Chen et al. ( 2020 ); Liu et al. ( 2020b ) found that correlation coefficients between population emigration from Wuhan ranged from 0.597 to 0.996 depending on regions and mobility patterns. Moreover, a significant and positive association was observed between public transportation daily frequency—including flights, buses, and trains operating from Wuhan—and the number of COVID-19 cases (Zheng et al. 2020 ).

Case studies outside China showed similar patterns (Jaekel and Muley 2022 ; Yang et al. 2021 ; Cintia et al. 2020 ). Mobility patterns derived from mobile phone data in 25 US counties showed a strong correlation, with Pearson correlation coefficients above 0.7 for 20 of the 25 counties (Badr et al. 2020 ; Kissler et al. 2020 ) observed that the mean estimated prevalence of COVID-19 infection by borough in New York City was strongly negatively correlated with reduced commuting (-0.88Iacus et al. ( 2020b ) also confirmed that human mobility (internal and outbound movements) had a high impact on initial virus spread in case studies of France, Italy, and Spain, with between 52% and 92% in France, up to 91% in Italy, and up to 75% in Spain. Kartal et al. ( 2021 ) revealed a cointegrated relationship between mobility and pandemic indicators through the Toda-Yamamoto causality test.

There are studies examining COVID-19’s impact on each type of transport mode to evaluate the contribution of different transport modes to virus spread. Several studies found a strong correlation between air traffic (e.g., airline passenger, number of airports, and flight routes) and COVID-19 spread (Su et al. 2022a , b ; Lau et al. 2020 ; Sokadjo and Atchade 2020 ; Oztig and Askin 2020 ) found that both domestic and international passenger volumes in China were strongly associated with domestic and international COVID-19 cases. The results indicated that adequate measures are necessary to prevent a long-term crisis, such as on-site disease detection, temporary passenger quarantine, and limited air traffic operation. Oztig and Askin ( 2020 ) employed a negative binomial regression analysis on data from 144 countries, including population density as a control variable, and found a positive association between high numbers of airports in a country and high numbers of infected patients. A strong and significant association was also found between travel volume by train and the number of confirmed COVID-19 cases (Pang et al. 2023 ; Zhao et al. 2020b ) found that one more high-speed railway (HSR) train originating from Wuhan each day increases the cumulative number of COVID-19 cases in a city by about 10%. Zhao et al. ( 2020b ) estimated that a 10% increase in the number of train passengers from Wuhan to major cities in China resulted in an 8.27% increase in infections. However, cars and flights were not statistically significant in the study. Conversely, Zhang et al. ( 2020b ) presented slightly different results. Flight and high-speed train frequencies in and out of Wuhan, China were positively and significantly associated with number of confirmed cases in the destination cities at the level of 1% and 10%, respectively. In contrast, coach (inter-city bus) services were not significantly associated with imported confirmed cases, presumably because most of coach travelers use the service for relatively short trips. Therefore, the authors argued that limiting air transport from a pandemic center is the first measure to employ to reduce travel related imported infections. Another study suggests that accessibility is related to the spread of COVID-19. Carteni et al. ( 2021 ) focused on the hypothesis that areas with higher accessibility were more easily reached by the virus. Based on data from Italy, the regression model showed that transport accessibility, population, population density, and particulate matter (PM), were significantly related to COVID-19 cases. Rail-based transport accessibility (39.7% in weight) was the best predictor for number of COVID-19 infections, followed by population and population density (about 14%), and territorial and pollutant variables (9.3%).

Impact of COVID-19 on overall mobility

Because of the COVID-19 pandemic and government countermeasures, all cities worldwide experienced reduced traffic volumes, which may have resulted from various causes, such as deceased voluntary outside activities owing to fear of COVID-19 infection, and/or government orders (i.e., travel restriction, social distancing policies) implemented to mitigate spread.

A range of studies examined the impact of COVID-19 on mobility using different data sources and research perspectives (Table  2 ). The impact of COVID-19 on transportation demand was greatest in the early stages of the outbreak, and early studies focused on this. Gonzalez et al. ( 2021 ) found that public and private mobilities at the peak of the pandemic dropped to 95% and 86% of pre-COVID-19 levels in Spain. C2SMART (Connected Cities with Smart Transportation Center) releases monthly reports on mobility changes in New York and Seattle, US as case studies to analyze transit ridership, bridge and tunnel traffic, travel time, and number of crashes during the pandemic (Gao et al. 2020a , d ; Bernardes et al. 2020 ). After the stay-at-home order was implemented in New York, both transit ridership and general traffic volume dropped, with transit ridership severely impacted, dropping 94% in the peak period as of March 23, 2020 (Gao et al. 2020a ) compared to the 2019 statistics. It remained down at 91% in April (Gao et al. 2020d ), improving to 80% in the first week of July (Bernardes et al. 2020 ). Reduced traffic volumes owing to the stay-at-home policy resulted in a decrease of average travel times as well: dropped by 38% during the third week of February (Gao et al. 2020a ). In contrast, cycling increased by 55% in a temporary mode shift, and all traffic safety indicators improved (vehicle collisions dropped up to 77%, pedestrian injury/fatality decreased 51%, and cyclist injury/fatality in crashes decreased 31%) (Gao et al. 2020a ). A US city, Seattle, experienced similar COVID-19 mobility impacts (Gao et al. 2020d ). Highway traffic volume in the US state, Florida, also decreased by 47.5%, compared to the 2019 statistics (Parr et al. 2020 ). Korea’s average daily traffic volume in early 2020 also differed substantially from the 2019 volume, decreasing from 149 million vehicles in 2019 to about 144 million vehicles in 2020, a 9.7% decrease (Lee et al. 2020a ). Canada’s mobility trends showed a clear, large reduction in mobility to non-residential locations after the state of emergency was declared (Chen, 2020 ). COVID-19 also significantly reduced taxi trips, and affected taxi trips’ travel speed (increased by 29.4%), travel time (decreased by 22.6%), and average distance (increased by 2.4%) (Nian et al. 2020 ). Average daily taxi trips in February 2020 were only 11.3% of those in May 2019. Nighttime taxi trips (9 PM – 5 AM) were significantly impacted dropping to 8.5% of the normal period. The impact of COVID-19 was greatest at the beginning of the epidemic, and the next waves of the pandemic seem to be less than the initial ones (Pozo et al. 2022 ; Advani et al. 2021 ; Konecny et al. 2021 ; Rasca et al. 2021 ). For example, subway traffic in 2020 in the UK fell to 5% during the first lockdown (from April to July), recovered to 37% before the second lockdown, and then fell back to 25% during the second lockdown in November (Vickerman 2021 ).

While previous studies used various databases, extensive literature used aggregated location data obtained from mobile phones, including Google Community Mobility reports and Apple Mobility Trends reports, to quantify COVID-19’s impact (Askitas et al. 2020 ; Tirachini and Cats 2020 ; Carteni et al. 2020 ; Schlosser et al. 2020 ; Pullano et al. 2020 ; Klein et al. 2020b ; Gao et al. 2020b ; Yabe et al. 2020 ; Galeazzi et al. 2021 ; Santamaria et al. 2020 ; Iacus et al. 2020b ). Many researchers emphasized location data’s usefulness for modeling disease spread (Heiler et al. 2020 ), providing empirical evidence of human mobility (Couture et al. 2022 ), investigating the effects of different types of government interventions on human mobility, and monitoring the impact of such measures on the epidemic trajectory (Pepe et al. 2020 ). Using mobile device location data, Lee et al. ( 2020b ) found that US nationwide mobility trends changed rapidly around March 13, when the national emergency was declared, and daily movements in general decreased; the percentage of people staying home rapidly increased from 20% on normal days (benchmark week, Feb. 3 to Feb. 16, 2020) to 35% after the outbreak (Apr. 6 to Apr. 12, 2020); out-of-county trips decreased from 28 to 23%; average trip distance dropped from 40 miles to 23 miles; and number of trips per person decreased from 3.7 to 2.7. Based on Google Mobility Report data, even comparing two countries with different characteristics, Germany and Qatar, the impact on the transport sector (e.g., correlations between traffic volume and government measures) was found to be similar (Jaekel and Muley 2022 ). Using smart card and private vehicle records in Korea, Lee et al. ( 2023 ) found that trip frequency was significantly decreased during non-peak hours on weekdays and during weekends. In addition, private vehicle usage increased for shorter trip distances, while bus usage dropped regardless of trip distances. Mobile phone data and Google and Apple reports were also used for other studies to find a correlation between the outflow of people and the reported COVID-19 cases with an eight-day time lag (Heiler et al. 2020 ), develop daily time-series’ of different mobility metrics (Pepe et al. 2020 ), investigate the impact of COVID-19 on changes in community mobility and variation in transport modes during COVID-19 alert levels (Wen et al. 2021 ), and examine changes in population density and visualize spatial population distributions (Arimura et al. 2020 ).

A few studies developed models or simulations to investigate the impact of COVID-19 on future mobility (Peng et al. 2023 ; Wang et al. 2020 ). Using MATSim, an agent-based simulation model, and assuming that the mode preference during the pandemic is maintained, Wang et al. ( 2020 ) predicted that a full reopening scenario of the NYC transportation system would result in 73% of pre-COVID transit ridership owing to changed mode preferences, while increasing car traffic as much as 142% of pre-pandemic levels. When limiting transit capacity to 50%, transit ridership would decrease by as much as 64% of pre-COVID ridership, while increasing the number of car trips to as much as 143% of pre-pandemic levels.

Other studies examining COVID-19’s impact on mobility focused on heterogeneous impacts on socioeconomic demographics or across space (Pan and He 2022 ; Habib et al. 2021 ; Guzman et al. 2021 ) found significant inequalities between income groups with respect to access to essential services in Bogota. Lee et al. ( 2020b ) found that a higher income group was more likely to stay home after the national emergency declaration, and a higher density group tended to have lower trip distance after the outbreak. Ruiz-Euler et al. ( 2020 ) and Yang et al. ( 2021 ) also found different rates of reduced mobility owing to COVID-19 for high- and low- income groups, called the mobility gap. The second phase of the pandemic also showed heterogeneous changes in travel behavior according to individual attributes (e.g., age, gender, education level, marital status, income, etc.) (Jiao and Azimian 2021 ; Glaeser et al. 2022 ) estimated that total cases per capita decreased by 19% when mobility dropped by 10% in five US cities. The authors observed substantial heterogeneity across space and over time: east coast cities (i.e., NYC, Boston, and Philadelphia) had stronger effects than Atlanta and Chicago. For these differences, the authors presumed to reflect the initial infection rate rather than mobility characteristics.

Impact of COVID-19 on public transportation

The previous section confirmed that all countries worldwide experienced a pandemic related mobility drop, and public transportation was one of the most disrupted sectors (Table  3 ). A remarkable drop in public ridership was reported from many cities worldwide, with a 93% drop in the worst affected cities (Pozo et al. 2022 ; Medlock et al. 2021 ; Hasselwander et al. 2021 ; Gkiotsalitis and Cats, 2021 ; Aloi et al. 2020 ; Ahangari et al. 2020 ). Jiang and Cai ( 2022 ) found that for each additional local COVID-19 cumulative case within 14 days, subway ridership decreased by 0.091% in Beijing and 0.112% in Shanghai. Because public transport vehicles and stations are perceived as high risk, and fear of contagion between travelers was related to higher passenger density in a limited physical space, governments in many countries implemented restriction policies to limit or discourage public transport use, and some public transport operators reduced their services (Marra et al. 2022 ; Kłos-Adamkiewicz and Gutowski 2022 ; Jenelius and Cebecauer 2020 ; Tirachini and Cats 2020 ; Gkiotsalitis and Cats, 2021 ). However, public transportation is one of the most important modes of mobility, because it is sustainable and transports people on a large scale. Many transit dependent riders do not have access to a private vehicle (Pawar et al. 2020 ; Shakibaei et al. 2020 ), especially low income and historically marginalized people, who experience further loss of mobility when public transport is restricted (Suman et al. 2020 ; Wilbur et al. 2020 ; Shaheen and Wong 2020 ).

Regardless of the public transit transmission risk controversy, when traffic volume decreases owing to COVID-19, the reduced ridership impact is much more severe than mobility changes related to private cars. Several studies focused on this issue, examining unprecedented decline in demand and revenue, limited capacity, and social equity (Pozo et al. 2022 ; Shelat et al. 2022 ; Hasselwander et al. 2021 ). For example, public transport ridership decreased by about 80%, while the percentage of people using a car increased from 43 to 65%, and cycling (reduced by 23%) and bike sharing (reduced by 2%) were not significantly impacted in Budapest, Hungary (Bucsky 2020 ). A similar trend was reported in New York City. Subway ridership dropped 96% on April 12, 2020, compared to that before the pandemic (Kaufman et al. 2020 ). Commuter rail use in New York (dropped up to 97.9% compared to 2019 levels) was the most significantly affected by the pandemic, followed by subway (91.7%), buses (78.3%), and vehicle traffic volume for bridges and tunnels (65.5% by the end of May). In three regions of Sweden, which relied on recommendations instead of government mandates, public transport ridership was severely impacted (declining by 40% in Vastra and Gotland and 60% in Stockholm) (Jenelius and Cebecauer 2020 ). Public transit users changed their mobility patterns by switching from monthly period tickets to single tickets and travel funds (Jenelius and Cebecauer 2020 ; Orro et al. 2020 ) found that bus ridership in Coruña, Spain, was only 8–16% of 2017–2019 ridership. Lozzi et al. ( 2020 ) found that public transit dropped by 76% in April 2020 in 62 countries and 89 cities, compared to a baseline date of January 13, 2020.

Air transportation was also severely impacted by COVID-19, because many countries implemented international travel bans. Commercial flight operations were dramatically reduced worldwide, with over two thirds fewer flights than in the same period in 2019 (Falchetta and Noussan 2020 ). Major airline carriers’ capacity dropped by 60–80% and airline industry job loss was estimated around 7% (Sobieralski 2020 ). Moreover, the study estimated that recovery from the adverse effects of the current uncertainty shock will take between four and six years. Iacus et al. ( 2020a ) forecast air traffic volume and analyzed travel bans’ impact on the aviation sector using historical air traffic data, real time flight tracks, and online booking systems data.

Some studies investigated COVID-19’s heterogeneous impact on public transport users with different socioeconomic-demographic characteristics. A study shows that older people and female travelers are more likely to be conscious of COVID-19, while those who report using the train more often tend to be indifferent to infection (Shelat et al. 2022 ; Almlof et al. 2021 ) found that public transport use decreases were associated with income levels, house ownership, and high employment levels. Similarly, Liu et al. ( 2020c ) found uneven impacts on transit systems and social groups in an analysis of 113 public transit systems in US communities. The study showed higher levels of transit demand during the pandemic in areas with higher proportions of essential workers, vulnerable populations (African American, Hispanic, female, and people over age 45), and more coronavirus Google searches. In a case study of Nashville and Chattanooga, TN, US, fixed-line bus ridership dropped by 66.9% and 65.1%, respectively, with a significant impact on low-income groups (Wilbur et al. 2020 ; Nikolaidou et al. 2023 ; Ahangari et al. 2020 ) investigated factors affecting public transport ridership, including the cleanliness of public transport, income inequality index, unemployment rate, poverty, education, and percentage of foreign-born residents.

The COVID-19 pandemic also led to changes in public transit services, where some public transport operators reallocated their services and provided minimum operations to meet essential travel demands, while considering government regulations and maintaining a safe transport mode (Limsawasd et al. 2022 ; Tiikkaja and Viri 2021 ; Meena 2020 ; Tirachini and Cats 2020 ; Ahangari et al. 2020 ). For example, Milan and Barcelona reduced vehicle occupancy to a maximum of 25% and 50%, respectively. Catalonia provided app users with bus occupancy levels in real time. The city of Hamburg adopted flexible bus routes to increase service on the busiest routes and reduce service frequency on lower demand routes (Lozzi et al. 2020 ). Gkiotsalitis and Cats ( 2022 ) and Suman et al. ( 2020 ) developed optimization models to redesign public transport services such as optimal service frequencies.

Other impacts: bicycles, shared mobility, environment and traffic safety

As reviewed in previous sections, most research involved on case studies of the impact on personal and public transportation. Relatively few studies have examined the impacts of COVID-19 on other transport modes, such as bicycles and shared mobility. The usage behavior of these modes shows inconsistent results in each city, probably because the factors of decrease (e.g., decreased numbers of trips and increased working from home) and increase (e.g., effects of short-distance travel shifting from public transit) are mixed together. For example, during the pandemic, bike-sharing use decreased in London (Li et al. 2021b ; Heydari et al. 2021 ), Lisbon (Teixeira et al. 2022 ), Bangkok (Sangveraphunsiri et al. 2022 ), and Slovakia (Kubal’ák et al. 2021 ), remained moderately stable in Korea (Choi et al. 2023 ), and increased in Singapore (Song et al. 2022 ) and Washington DC (Chen et al. 2022b ). Ten cities in Germany also showed inconsistent results; the bicycle traffic volume decreased where the ratio of bicycle means was high and increased where the ratio of means was low, while pedestrian traffic decreased with higher local infectiousness and government measures (Mollers et al. 2022 ). In the case of London, shared bicycle usage immediately decreased due to the effect of the first lockdown but bicycle use increased during the lockdown period and showed a much larger increase after the first lockdown was lifted (Li et al. 2021b ). Interestingly, morning peak travel and short-time travel by public bicycles in London maintained a low level of use during the lockdown and easing periods but were significantly higher at other times of the day and travel with middle and long duration. According to the study on the change in the travel behavior of bicycle sharing in Bangkok, shared bicycles were mostly used for business travel during the morning and afternoon peak hours on weekdays and leisure on weekends before COVID-19 (Sangveraphunsiri et al. 2022 ). However, the number of bicycle trips connecting subway stations in major university districts increased significantly after the pandemic.

There were also a few studies on changes in shared transportation and micro-mobility, and it was found that ridership was mostly decreased due to COVID-19 (Li et al. 2021c , d ; Teixeira and Lopes 2020 ). For example, shared mobility ridership decreased by about 35% compared to normal in India (Meena 2020 ). In the case of Beijing, the overall share of shared mobility was kept constant between 36% and 38% both before and after COVID-19, but the proportion of ride-sharing decreased by 4.5% after COVID-19, while that of ride-hailing, car sharing, and bike sharing increased by 3.11%, 2.02%, and 0.89%, respectively.

Studies that examined COVID-19’s impact on environment and safety demonstrated that travel restrictions and reduced travel activities owing to COVID-19 resulted in improved air quality and safety (Llaguno-Munitxa and Bou-Zeid 2023 ; Nian et al. 2020 ; Muley et al. 2020 ; Cui et al. 2020 ; Sasidharan et al. 2020 ). Many studies have shown significant reductions in vehicle fuel consumption and emissions (Fischedick et al. 2021 ; Aloi et al. 2020 ). Vehicle emissions were estimated to decrease by 88.4% in 2020 and 48.6% in 2021 in Slovakia (Harantová et al. 2022 ) and by 14% in India (Advani et al. 2021 ). GHG emission was also estimated to decrease by 64% during the lockdown in Canada (Alama et al. 2022 ). COVID-19 and travel restriction policies have had a positive impact on traffic accidents, dropping by 67% in Santander, Spain (Aloi et al. 2020 ), 41% during the first month of COVID-19 in Greece, and 76% during the lockdown (March 16 – April 26, 2020) compared to 2018–2019 in Spain (Saladie et al. 2020 ).

Changes in personal travel behavior based on surveys

The literatures reviewed in the previous chapter were mainly studies based on observed data. It is necessary to survey to analyze changes in personal travel behavior due to COVID-19 or the corresponding government’s measures. In this chapter, we review studies on this topic that were not revealed in aggregated data. Travel restrictions are effective tools for controlling infectious disease spread at the initial stages, while behavioral changes are important to limiting spread at a later stage (Muley et al. 2020 ). In addition to unprecedented total mobility reductions, the pandemic drastically impacted activity patterns and travel behavior through government implemented travel restrictions and individuals’ perceptions of safety and health (Table  4 ). These changes include transport mode choice (i.e., preferring more active and non-motorized modes), travel patterns (i.e., reducing non-essential trips and increasing work from home), and activity behavior (i.e., reducing outdoor activity and increasing online shopping) (Jou et al. 2022 ; Puelo, 2022; Nikiforiadis et al. 2022 ; Bhaduri et al. 2020 ; de Vos 2020 ; Moslem et al. 2020 ; Campisi et al. 2020 ; Borkowski et al. 2021 ; Tan and Ma 2021 ; Shamshiripour et al. 2020 ). Trip purpose, distance traveled, and trip frequency also changed (Abdullah et al. 2020 ). Significant predictors of mode choice included gender, car ownership, employment status, travel distance, primary purpose for travel, and pandemic-related underlying factors (Abdullah et al. 2020 ).

Many studies conducted preference surveys and demonstrated significant travel behavior changes with different perspectives and impacts on sociodemographic groups (Ferreira et al. 2022 ; Javadinasr et al. 2022 ; Szczepanek and Kruszyna 2022 ; Downey et al. 2022 ; Zhou et al. 2022 ; Currie et al. 2021 ; Echaniz et al. 2021 ; Abdullah et al. 2020 ; Morita et al. 2020 ; Tan and Ma 2021 ; Shakibaei et al. 2020 ; Ghader et al. 2020 ; Przybylowski et al. 2021 ; Pan et al. 2020 ). All survey results indicated that the pandemic impacted mode choice behavior, with people avoiding crowded places to maintain social distance, which resulted in significantly reduced public transport and shared mobility demand owing to health concerns, and increased dependence on private vehicles (Oestreich et al. 2023 ; Nian et al. 2020 ). In Santiago (Astroza et al. 2020 ), overall trips were reduced by 44% (with the highest reduction in metro (55%), ride-hailing (51%), and bus (45%)). Transport modes relatively less affected by COVID-19 were motorcycle (28%), auto (34%), and walking (39%). While 77% of workers from low-income households had to go out to work, 80% of workers from high-income households worked from home. In the UK, 81.9% of private commuters responded that they would continue to use their car even when restrictions are lifted, while only 3.6% and 6.5% said they could switch to walking and biking, respectively (Harrington and Hadjiconstantinou 2022 ). On the other hand, public transportation users from diverse locations in the world were 31.5, 10.6, and 6.9 times more likely to change their commuting mode than car users, motorcycle users, and pedestrians, respectively (Dingil and Esztergár-Kiss 2021 ; Bhaduri et al. 2020 ) analyzed the effect of traveler’s sociodemographic characteristics on travel mode choice. About 95% of respondents said that both their daily commute and discretionary travel behavior were affected by the pandemic. Meena ( 2020 ) analyzed the impact of COVID-19 on travel patterns during normal, pre-lockdown, and post lockdown periods and found that private car use increased during pre-lockdown (21%) and was expected to increase more significantly during the post lockdown period (31%), compared to the normal situation (17%). Although there were differences in degree, mode choice changes were similarly observed in other surveys, including Palermo and Catania in Italy (Moslem et al. 2020 ), Istanbul in Turkey (Shakibaei et al. 2020 ), Gdansk in Poland (Przybylowski et al. 2021 ), and China (Tan and Ma 2021 ). The COVID-19 pandemic has also changed the factors influencing mode choice (Das et al. 2021 ). Prior to COVID-19, factors such as travel time saving, safety, security, and comfort (Zubair et al. 2022 ) and factors including distance and duration of travel (Mussone and Changizi 2023 ) were important influencing factors in the choice of modes, but during the pandemic, concerns about infection, social distancing, wearing a mask, and worry about using public transport became important influencing factors (Zubair et al. 2022 ; Mussone and Changizi 2023 ). Studies also show that comfort and frequency of transit (Costa et al. 2022 ), the availability of vaccines, and the obligation to wear a mask onboard (Mashrur et al. 2022 ) affect the choice of public transit. Mancinelli et al. ( 2022 ) investigated a change in travel patterns departing from airports and ports. They found that before COVID-19, about 73% of respondents used public transportation as an accessibility mode, but the proportion was less than 50% during the pandemic, and the intention to use public transportation after COVID-19 surveyed to be about 56% (Mancinelli et al. 2022 ).

While many studies focused on mode choice behavior changes related to the pandemic, other studies investigated travel characteristics, such as travel distance. In Switzerland, compared to 2019, the travel distance of all means of transportation decreased by 50% at the beginning of the outbreak, and when the first restriction was implemented, the travel distance of public transportation decreased by more than 90% (Hintermann et al. 2023 ; Marra et al. 2022 ; Meister et al. 2022 ). Using a survey distributed in various countries, Abdullah et al. ( 2020 ) found that the percentage of respondents who traveled for a short trip (a distance less than 10 km) dropped from 71% before the pandemic to 45% during the pandemic. The average work trip distance was 3.6 km and 2.6 km before and during the pandemic, respectively. In fact, these numbers are much smaller than expected, probably due to the analysis of diverse countries, including underdeveloped countries. Travel distance differences before and during the pandemic were also reported by other studies (Borkowski et al. 2021 ; Bounie et al. 2020 ; De Haas et al. 2020 ).

Travel behavior is a complex issue, influenced by various factors such as sociodemographic and personal characteristics (Simovi´c et al. 2021 ; Abdullah et al. 2021 ; Jiao and Azimian 2021 ; Borkowski et al. 2021 ). When investigating the mode choice behavior before and during the pandemic, Abdullah et al. ( 2022 ) found that during the pandemic, monthly household income and epidemic-related factors were important predictors for short-distance (i.e., < 5 km) mode choice, whereas gender, car ownership, and monthly household income were significant predictors for longer distances (i.e., > 5 km). In a survey administered in Sicily, Italy, women were 1.5 times more likely to reduce walking frequency than men (Campisi et al. 2020 ; De Haas et al. 2020 ) found that about 80% of respondents in the Netherlands panel data reduced their outdoor activities. In particular, older people tended to reduce activities more than before the pandemic. Travel behavior changes in terms of out-of-home travel activities, activity purposes, and travel differences by income level were also observed in Canada (Fatmi 2020 ). Respondents in Lagos, Nigeria showed a positive correlation between transportation influenced by COVID-19 and its impact on economic (correlation coefficient of 0.442), social (0.313) and religious (0.274) activities (Mogaji 2020 ).

Working from home (WFH) increased, emerging as one of the government policies during the pandemic (Hensher et al. 2022 , 2023 ; Ecke et al. 2022 ; Mouratidis and Peters 2022 ; Balbontin et al. 2022 ; Beck and Hensher 2020a , b ). About 71% of Chicago US respondents reported that they had not experienced working from home before the pandemic, while about 63% reported that they did experience working from home during the pandemic (Shamshiripour et al. 2020 ). The value of travel time has changed due to the WFH policy, increasing by 12.55% compared to before the pandemic in Australia (Hensher et al. 2021 ). Using GPS tracking data in Switzerland, Huang et al. ( 2023 ) found more significant reductions of trip distance, travel time, travel frequency, morning peak hours trips, and trips to the CBD among the WFH group. Promoting WFH also decreased traffic congestion, especially during morning peak hours, in Hong Kong (Loo and Huang 2022 ). The main factor influencing WFH during the lockdown period in the Netherlands was job characteristics; office workers and teaching staff were more likely to spend more time working from home (Kalter et al. 2021 ). In a study analyzing WFH patterns using data from eight countries, the results show that the role of socioeconomic characteristics differs from country to country (Balbontin et al. 2021 ). In South America, for example, older adults and women are more likely to have WFH compared to other countries analyzed, and income has a positive effect on the number of WFH days in Australia and Chile. However, an issue of inequity was revealed as low-income and low-educated people were mainly unable to WFH and did not have flexible working hours (Ecke et al. 2022 ).

Fear of contagion and perceived risk also significantly impacted travel patterns (Airak et al. 2023 ; Navarrete-Hernandez et al. 2023 ; Zavareh et al. 2022 ; Aghabayk et al. 2021 ; Przybylowski et al. 2021 ; Abdullah et al. 2020 ). Awareness of overcrowding during the COVID-19 pandemic is about 1.04 to 1.23 times higher than before the pandemic (Cho and Park 2021 ). Women tend to be more sensitive than men to fear of infection and the use of face masks on public transport (Basnak et al. 2022 ; Schaefer et al. 2021 ). On the other hand, younger and low-income people are relatively less sensitive to overcrowding (Basnak et al. 2022 ). When exploring risk perception effects on human mobility for 58 countries using Global Preferences Survey data, Chan et al. ( 2020 ) found that regions with risk-averse attitudes were more likely to adjust their mobility behavior in response to the WHO declaration of a pandemic even before official government lockdowns. Przybylowski et al. ( 2021 ) found that willingness to use public transport depended mostly on perceived comfort and safety during the pandemic. Parady et al. ( 2020 ) examined pandemic related factors affecting behavioral changes in non-work-related activities in Japan, which focused on the effects of risk perception and social influence. Yuksel et al. ( 2020 ) conducted a case study in Canada that examined behavioral parameters of change in mobility and sentiment that reflected people’s beliefs about how contagious the disease is on the level of compliance with public orders. Mode choice behavior changes might be maintained for a long time owing to concerns about infection risk (Nian et al. 2020 ). Although Hotle et al.‘s ( 2020 ) survey was not conducted during the COVID-19 pandemic, the authors found that a recent personal experience with influenza symptoms resulted in higher risk perception at mandatory and medical trip locations in women, while men were not likely to change their travel patterns in response to potential virus spread or increasing exposure. Interestingly, high perceived workplace risk did not significantly reduce individuals’ travel to their workplaces. In addition, when Pawar et al. ( 2020 ) investigated the impact of COVID-19 on mode choice during the transition to a lockdown period in India, they found that commuters’ safety perceptions did not have a significant effect on transportation mode choice.

Effects of measures on mobility reduction and COVID-19 spread

The COVID-19 pandemic presented an unprecedented challenge to governments, forcing them to implement various non-pharmaceutical countermeasures to reduce the possibility of contact and minimize disease transmission. Such interventions included complete city lockdowns, travel restrictions, stay-at-home policies, some location closures, and social distancing policies Rosik et al. 2022 ; Zhang et al. 2021a ; Gkiotsalitis and Cats, 2021 ; Gao et al. 2020c ; Yabe et al. 2020 ; Wielechowski et al. 2020 ; Schwartz 2020a ). Numerous countries introduced different types and degrees of restrictive policies (e.g., from complete lockdown in China, lockdown in Italy, Spain, and France, to mild and less restrictive policies in Sweden, Netherlands), which influence people’s lifestyles, social interactions, travel behaviors, and activity behaviors (Borkowski et al. 2021 ; Abdullah et al. 2020 , de Haas et al. 2020 ; Klein et al. 2020a >; de Vos 2020 ).

The impact of such interventions on transportation systems, travel behavior, and COVID-19 spread has drawn much research attention (Table  5 ). According to Jaekel and Muley ( 2022 ), reduced traffic volumes were more associated with restrictive measures than COVID-19 incidences in both Germany and Qatar. However, the relationships between the measures and travel behavior changes in response to COVID-19 are complex. Glaeser et al. ( 2022 ) emphasized that evaluating the effectiveness of restrictions on mobility is challenging for several reasons: the restriction policies are adopted to limit the spread of outbreaks, while individuals make decisions on travel based on their personal attitudes regarding risk of contagion. It is also important for policy makers to understand the efficacy of restriction policies in any given time and region to prepare for future disease outbreaks (Yuksel et al. 2020 ). Accordingly, this chapter reviews studies explicitly, analyzing the effects of various implemented policies, and discusses them under three topics.

Travel restriction policies’ impacts on reducing human mobility

During the pandemic, most cities around the world are experiencing a decrease in traffic volume, resulting from a combination of restrictive policies rather than the impact of the outbreak itself (Jaekel and Muley 2022 ), because many epidemic prevention and control policies involve travel and activity restrictions. Global statistical data indicated that the restriction policy has substantially reduced transport demand. In particular, China’s city lockdown policy is unprecedentedly strong in the world, showing that it has the effect of controlling traffic and preventing the spread of COVID-19. For example, the Wuhan lockdown reduced inflows by about 77%, outflows by about 56%, and within-Wuhan movements by about 56% (Fang et al. 2020b ). In addition, without the Wuhan lockdown, it was estimated that the number of positive COVID-19 cases would be 105% higher (Fang et al. 2020b ). Although not as strong as China, several other countries have implemented city lockdowns and have shown effectiveness in controlling traffic (Jaekel and Muley 2022 ; Mars et al. 2022 ; Hadjidemetriou et al. 2020 ): that is, lockdown restrictions reduced (1) human mobility by 65% in France (Pullano et al. 2020 ), (2) mobility rate by 74.2% (21.7 trips/week before the pandemic vs. 5.6 trips/week during the lockdown) in Spain (Mars et al. 2022 ), and (3) long-distance travel in Germany (Schlosser et al. 2020 ).

However, several case studies show that a lockdown is not the only effective means of reducing traffic volume. According to the study analyzing public transport demand in Chile using smart cards (Gramsch et al. 2022 ), when the first measures (e.g., schools suspended in-person classes) were implemented at the beginning of the pandemic, the demand for public transport decreased by 72.3% compared to the year 2019, while it decreased by 12.1% with the dynamic lockdown implemented by each city. In particular, the effect of the lockdown decreased five weeks after its implementation, suggesting that the lockdown policy effectively controls the traffic volume in a short period of time. In this sense, the results of Dahlberg et al. ( 2020 )’s study analyzing Sweden’s less restrictive policy are interesting. When using mobile phone data to investigate COVID-19 causal effects, they found that even less restrictive and mild public recommendations convince people to comply with social distancing and avoid unnecessary travel (i.e., residential area daytime population increased by 64%; industrial and commercial area daytime population decreased by 33%; travel distance decreased by 38%; share of short trips less than one kilometer from home increased by 36%; and mobility change effects did not differ across socioeconomic and demographic characteristics). In addition, when comparing lockdown measure effects on mobility patterns in France, Italy, and the UK, Galeazzi et al. ( 2021 ) found that their mobility patterns differed in response to the travel restrictions owing to differences in existing infrastructure characteristics and initial mobility structure.

Besides strong restriction measures, such as city lockdowns and travel restrictions, several studies investigated the effects of less restrictive or non-compulsory policies. When examining the mobility impact of different non-pharmaceutical countermeasures for 41 cities worldwide, Vannoni et al. ( 2020 ) found that the decrease in mobility is 18% due to closing public transport, 15% due to workplace closures, 13.3% due to restricting internal movements, 10% due to school closures, and 7.09% due to canceling public events. In Tokyo, non-compulsory policies, such as remote working with private companies and school closures, reduced human mobility and social contact by about 50% and 70%, respectively (Yabe et al. 2020 ). Similarly, government interventions reduced overall mobility by about 50% in several major US cities (Klein et al. 2020b ) and reduced all station ridership by about 40.6% in Seoul, Korea (Park 2020 ). National social distancing measures were effective for intra-city vehicle movement, particularly at night, but not for inter-city movement in Korea (Sung 2022 ). Stay-at-home orders in the U.S. and Japan also showed a positive effect in reducing mobility (Liu and Yamamoto 2022 ; Gao et al. 2020c ). Analysis using mobile phone data showed that counties without stay-at-home orders in the U.S. reduced mobility by 52.3%, while counties with stay-at-home orders experienced a slightly larger mobility drop at 60.8% (Dasgupta et al. 2020 ).

The social distancing policy of maintaining at least six feet between people emerged as a widely accepted non-pharmaceutical intervention to mitigate the pandemic (Chen et al. 2022a ; Liu et al. 2022 ; Vichiensan et al. 2021 ; Zhang et al. 2020b ; Morita et al. 2020 ). Although social distancing might negatively affect subjective well-being and limit physical activity, many studies supported the positive effects on travel behavior to prevent social contact and COVID-19 spread (De Vos 2020 ; Fang et al. 2020b ). The public geo-located US Twitter data showed a significant 61.83% overall travel reduction after social distancing policies were implemented (Xu et al. 2020 ). In particular, larger reductions were found in states that were early adopters of social distancing practices, whereas smaller changes were found in states without such policies. Analysis using Google Community Mobility reports in the US showed that state-of-emergency declarations had only a modest effect on mobility (about a 10% decrease), but implementing one or more social distancing policies resulted in an additional 25% mobility decrease (Wellenius et al. 2021 ).

The implications of the studies discussed above are: (1) the effects of coercive policies are effective in the short-term (Liu et al. 2022 ); (2) policy announcement and implementation timing are important because unexpected anomalous behaviors can occur (Pullano et al. 2020 ; Liu et al. 2020b ); and (3) it is more effective to introduce a combination of different types of control policies (Wang et al. 2022 ; Chinazzi et al. 2020 ; Anzai et al. 2020 ; Wellenius et al. 2021 ).

The relationship between travel restriction policy and COVID-19 transmission

Governments implemented control and prevention policies to decrease traffic volume and person-to-person contact, which ultimately lead to reduced disease spread. Various studies examined the effectiveness of measures developed to limit COVID-19 spread and found that travel restrictions and social distancing directly affected travel behaviors, thus, effectively slowing COVID-19 spread, but at different levels (Chen et al. 2022a ; Wang et al. 2022 ; Manzira et al. 2022 ; Espinoza et al. 2020 ; Zhang et al. 2020b ; Chen and Pan 2020 ). The number of daily COVID-19 cases in Italy was directly associated with trips taken three weeks before (Carteni et al. 2020 ). In addition, the population outflow distribution significantly influenced the spatiotemporal distribution of confirmed COVID-19 cases in Wuhan, China, and the authors argued that the effect of quarantines on mobility to limit COVID-19 transmission was obvious (Jia et al. 2020 ).

There have also been studies showing that local travel restrictions were effective for controlling COVID-19 infection. In China, where the spread of COVID-19 was most severe, the suspension of high-speed rail and air connectivity with Wuhan reduced the number of daily new confirmed cases by 18.6% and 13.3%, respectively (Zhu and Guo 2021 ). According to the scenario simulation results using data from China, if lockdown and decreased population mobility policies were not implemented, the total number of infectious cases would have reached 138,824 in February 2020, corresponding to 4.46 times the actual case number (Wei et al. 2021 ). Travel restrictions implemented by local cities outside Hubei also decreased confirmed cases by 22.4% in the first two weeks after the Wuhan lockdown (Liu et al. 2020b ). Without intra-city travel restrictions, the confirmed cases were estimated to increase by 33.1%. Based on these results, the authors asserted that if travel restrictions were implemented in advance in the entire Hubei province, the number of confirmed cases might have decreased by another 10.5%, emphasizing the importance of a timely and coordinated response to mitigate the pandemic. Another study found that travel restrictions may have reduced expected cumulative incidence by 39% in Wuhan by February 29, 2020 (Shi and Fang 2020 ). Staying in the same county also effectively limited COVID-19 cases and deaths. US data showed that staying in the same county reduced total weekly COVID-19 cases by 139,503, and deaths by 23,445 (Yilmazkuday 2020 ).

Other studies suggest that simply implementing one restriction measure does not have a significant effect on decreasing new infections. According to Chinazzi et al.’s ( 2020 ) transmission model, 90% travel restrictions to and from mainland China only modestly affected pandemic spread, delaying it for two weeks at best, unless it was combined with a strong reduction (i.e., 50% or higher) in community transmission. China and worldwide data analyzed with the susceptible-exposed-infectious-recovered (SEIR) model showed that more rigorous government control policies were associated with a slower infection rate, and isolation and quarantine procedures were less effective for controlling the pandemic (Fang et al. 2020a ). When estimating the impact of travel restrictions, including lockdown in Wuhan, China, on COVID-19 incidence, Anzai et al. ( 2020 ) found that the estimated delay was smaller than the authors expected depending on the scenario. Therefore, they argued travel restriction decisions, such as a complete lockdown, should be carefully applied by comparing the resulting estimated epidemiological impact and predicted economic outcomes. Pan et al.’s ( 2020 ) analysis of mobile phone location data showed a similar trend. The authors proposed a social distancing index, which indicated that both government orders and local outbreak severity were significantly associated with the strength of social distancing.

Controversial issues related travel restriction policy effects

Since the COVID-19 outbreak, many studies have examined and explained the effectiveness of government control and prevention measures. However, the effectiveness of diverse measures has been a subject of debate (Anzai et al. 2020 ) because studies report inconsistent results or findings showing less effectiveness than expected, and identify controversial issues, such as control strategy side effects. Moreover, it is difficult to quantify and distinguish measure effects from other potential contributing factors (Liu et al. 2022 ; Fang et al. 2020b ). It may be unreasonable to draw one conclusion based on a single standard in this study because the timing and method of government policy implementation, citizen compliance, and analysis data and methodologies are different in each city. Nevertheless, it is of great significance to review the research results published so far and to learn some lessons. Accordingly, this section reviews the related issues and conflicting research results.

COVID-19’s spatial distribution in China was well explained by human mobility data in the early stages of the pandemic (until February 10, 2020) outside of Wuhan, China (Kraemer et al. 2020 ). However, after control measures were implemented, this correlation dropped and pandemic growth rates became negative in most China locations. The authors asserted that travel restrictions may have effectively reduced the flow of case importations from Wuhan in the early stages of the pandemic. However, restrictions may have been less effective once the outbreak was more widespread, thus other local mitigation measures may have been more important to mitigating spread. Another study using data from China also found that the fastest and most widespread way to prevent the spread of COVID-19 infection is to control the route connected to the epicenter in the early stages of the epidemic (Lu et al. 2021 ). If the virus is widespread, implementing restrictions in hub cities is much more efficient than imposing the same travel restriction across the country (Lu et al. 2021 ). Another interesting simulation determined when mobility restrictions effectively reduced the pandemic’s size within and between heterogeneous neighboring communities, including one with a high infection risk and another with a low infection risk (Espinoza et al. 2020 ). The study found that the number of secondary cases increased with the level of mobility, increasing the overall final pandemic magnitude. However, the cordon sanitaire did not always minimize the overall number of infected individuals. Accordingly, the authors argued that mobility restrictions may not always effectively contain disease spread that is evaluated by overall final pandemic size.

A few studies compared the effectiveness of various measurements. Martin-Calvo et al. ( 2020 ) used a SEIR model to evaluate the impact of different social distancing strategies under various what-if-scenarios for control and mitigation in Boston, US. The results showed that passive social distance strategies were not enough to contain the pandemic, while active strategies (i.e., large scale testing, remote symptom monitoring, isolation, and contact tracing) are needed. In addition, full confinement was not feasible and did not solve the problem without active measures in place after confinement in case a new outbreak occurred. Askitas et al. ( 2020 ) conducted a similar study that examined the impact of various non-pharmaceutical interventions on COVID-19 incidence and mobility patterns for 135 countries. The findings showed that canceling public events and restricting gatherings had the largest effects on limiting the pandemic. Workplace and school closures and stay-at-home requirements also had an effect, but it was not as large. Conversely, internal movement restrictions, public transport closures, and international travel controls did not have a significant impact on reducing new infections.

City lockdowns and travel bans are also controversial and do not always successfully control COVID-19 infections. Based on the susceptible-infection-recovery (SIR) model, Zhang et al. ( 2020a ) argued that lockdown measures, for example those adopted by China, have a severe social-economic cost and may not be a feasible solution for other countries, because there was no strong connection between population flow and cross-regional infection except at the very early stage of the outbreak. The authors asserted that non-lockdown-type measures may have outcomes similar to lockdowns if the measures are quickly prepared and strictly executed. Muller et al. ( 2020 ) also claimed that a single restriction strategy (i.e., a complete removal of infections in childcare, primary schools, or workplaces) is not sufficient to control infection dynamics. In addition, the estimated delay of contagion was smaller than expected depending on the model scenarios (Anzai et al. 2020 ). Therefore, even if the results show positive effects, some measures do not work everywhere (Arellana et al. 2020 ).

Two interesting studies discussed unexpected effects of social distancing. US state and local government interventions decreased daily mobility by between 45% and 55% as of late April 2020, and person-to-person contact events decreased further by 65–75% on average (Klein et al. 2020a ). However, after social distancing guidelines expired on April 30, 2020, mobility and contact patterns increased slightly by 14% as of early May 2020. Ghader et al. ( 2020 ) observed a similar trend when examining COVID-19 and social distancing policy effects on human mobility in the US. They found that when COVID-19 cases first emerged (i.e., early- to mid-March, 2020), social distancing statistics (i.e., percentage staying home, number of trips per person, trip distance, etc.) began to improve, regardless of government social distancing orders. However, these statistics stopped improving after about two weeks, despite continuously increased COVID-19 cases and government stay-at-home orders. The authors called this unexpected mobility and COVID-19 case trend “social distancing inertia.” This phenomenon was universal throughout US states, despite different COVID-19 case timelines and government orders in each state. The authors concluded that: (1) those able to follow social distancing orders had already done so before government intervention was adopted, and (2) there is a natural behavior inertia on social distancing, which limits improvement related to social distancing (Ghader et al. 2020 ).

Although many governments discouraged public transit to limit COVID-19 spread, whether public transport actually spreads the virus is another debate, because there is currently a lack of comprehensive research or scientific evidence on that (Liu et al. 2022 ; Zhang et al. 2021c ; Bucsky 2020 ; Wielechowski et al. 2020 ) found a negative but insignificant relationship between human mobility changes in public transport and the number of confirmed COVID-19 cases in Poland, although the strength and statistical significance of the correlation varied substantially across regions. However, there was a strong, negative, and significant correlation between public transport mobility changes and the stringency of government anti-COVID-19 policies. Therefore, the authors argued that forced lockdowns effectively enforced social distancing in public transport, and government travel restrictions contributed to decreased mobility. However, Borsati et al. ( 2022 ); Schwartz ( 2020b ) concluded that there is no direct correlation between urban public transit ridership and excess mortality or COVID-19 transmission. When comparing 418 policy measures from six developed countries (Australia, Canada, Japan, New Zealand, the UK, and the US Zhang et al. ( 2021c ) also found that none of the measures in public health and transport is associated with a reduction of either cumulative deaths or cumulative infection cases. Based on US case studies, Schwartz ( 2020b ) emphasized that COVID-19 cases are primarily associated with local community spread, rather than public transit ridership rates. Furthermore, Musselwhite et al. ( 2020 ) argued that, although infectious diseases spread in dense public transport vehicles, this does not support the effectiveness of restricting public transport services to prevent spread. As with the influenza case, infection in the subway is very rare (Cooley et al. 2011 ), while the risk of infection within household contact may be greater (Williams et al. 2010 ).

Discussion on future research perspectives

Individuals and governments have worked to reduce the spread of COVID-19. Although it is difficult to pinpoint the transportation sector’s role and influence, it is necessary to analyze the various COVID-19 related factors in more detail to extract the direct influence of travel behavior changes. This chapter reviews the limitations of previous studies (especially the lack of data) and the need for future research directions related thereto and presents topics that require further research in the future.

While extensive studies presented comparative research using revealed data, most were aggregated data forms or surveys. Large scale aggregated data, obtained from Google and Apple mobility reports and mobile devices, are easy to use and great information sources for identifying overall mobility trends under different government measures. However, such aggregated data are not random, represent a small group of individuals, and cannot explain the exact population behavior or capture the interpersonal contacts (Wen et al. 2021 ; Wellenius et al. 2021 ; Moslem et al. 2020 ). In addition, aggregated data cannot capture physical proximity to other people (Dasgupta et al. 2020 ). To avoid sample-related biases, large scale surveys using rapid data collection technologies are needed (Pawar et al. 2020 ).

The absence of strong mobility correlations and evidence coupled with studies’ reliance on aggregated or sample data suggests a need for different approaches, for example, analysis of individual level data. Given that COVID-19 is spread through person-to-person contact and the impact may vary depending on individuals’ movements, demographic and socioeconomic characteristics, travel frequencies, activity frequency and locations, and health status (Shi and Fang 2020 ; Arellana et al. 2020 ; Chen, 2020 ). Microscopic analyses could be conducted with individual travel trajectory, POI, and GPS location data (Nian et al. 2020 ). Another approach involves developing an agent-based simulation to identify individual movements, incorporate contact tracing information, and examine individual infection potential (Heiler et al. 2020 ; Pan et al. 2020 ). However, technical challenges (i.e., location uncertainty or spatial error) and sampling bias still need to be addressed to improve accuracy (Gao et al. 2020c ).

While many researchers agree on some heterogeneity between COVID-19 prevalence and travel behavior, they emphasize the need for detailed spatiotemporal studies in different cities. Some studies analyzed spatial and temporal correlations, but analyses were limited by a lack of data (Zhao et al., 2020a ). Analyses on short- and long-term COVID-19 impacts on travel behavior and overall mobility are lacking (Heiler et al. 2020 ). In addition, few studies have considered post pandemic factors, especially since it is difficult to predict exactly when the pandemic will end. Most data and studies reviewed in this study were likely conducted in the early phase of the outbreak and may not represent whole waves; future studies should include more detailed and longitudinal studies that examine COVID-19 evolution over a longer period and measure how expectations, experience, activity, and travel behaviors change over time, both during the pandemic and after it ends (De Haas et al. 2020 ; Pan et al. 2020 ).

A more complex and intensive application that employs the most advanced technologies combined with qualitative analysis can provide a deeper understanding of travel behavior in response to COVID-19, limit continued virus spread, and provide information for developing adequate prevention policies to manage future diseases with pandemic potential. For a more thorough analysis of the spatiotemporal effectiveness and efficiency of the government’s COVID-19 measures related to travel restrictions, additional studies needed in the future are:

investigating changes in long-term and short-term travel behavior according to stages of the spread of COVID-19 and government measures;

extensive investigation of controversial issues (i.e., effects of travel restriction policies on virus infection);

in particular, regarding the estimation of infection risk levels in public transport, new evidence, and a thorough comparative analysis of previous research methodologies;

analysis of how the analysis methodology of existing studies can affect the results;

examining social distancing regulations’ implications for public transit (i.e., capacity or occupancy levels, a solution for expanding passenger demand);

analyzing different public transit funding mechanisms, such as optimal transit fare structures;

modeling a wide range of scenarios for shared mobility, paratransit modes, ride-sharing, ride-hailing, and carpooling;

assessing alternative transportation services in rural and low-income areas; evaluating social equity issues related to transport availability;

analyzing urban environment effects (i.e., land use and density) associated with travel patterns;

exploring geographic heterogeneity, such as comparing urban and rural areas and international experiences;

examining travel information’s effect on mitigating public transportation crowding; and.

developing more sophisticated interactive simulation platforms that use real time data and provide simulation outputs with adequate indicators under various scenarios.

Conclusions

The unprecedented COVID-19 outbreak significantly influenced nearly every aspect of daily life across the globe, resulting in dramatic changes in human mobility and activities, and leading to a preference for cars and active modes over public transport. Prevention strategies and travel related control policies also significantly affected urban mobility. Considering the pandemic’s magnitude and severity, both researchers and policy makers need to understand how people respond to the virus and to government restriction policies to reduce the potential for disease spread.

This study reviewed extensive evidence from many countries to investigate the relationship between human mobility and COVID-19 spread, COVID-19’s impact on mobility, and the effect of government countermeasures to reduce mobility to limit disease spread. Findings from this review are summarized as follows:

Although many studies investigated the effectiveness of government measures to limit mobility, controversy remains regarding whether a single restriction measure can effectively reduce COVID-19 cases.

City lockdowns and strict travel restrictions carry severe social-economic costs and may not be a feasible solution in some countries. No strong evidence emerged supporting a consistent connection between population flow and cross-regional infection except in a very early stage of the outbreak.

Government measures regarding social distancing in response to COVID-19 seemed to be effective, but there was not strong evidence supporting a strict limit on human movement itself.

Research in several countries showed that social distancing effectively reduced COVID-19 spread. There is general agreement that COVID-19 spreads through social activities in specific places (i.e., workplaces) and social gatherings after travel.

Preparing for a “new normal” after the pandemic is recommended. The pandemic’s long-term consequences may lead to a new era involving economic and social changes, such as smart working and other daily activity patterns, that may reduce future mobility needs.

Abdullah, M., Dias, C., Muley, D., Shahin, M.: Exploring the Impacts of COVID-19 on Travel Behavior and mode Preferences, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Abdullah, M., Ali, N., Javid, M.A., Dias, C., Campisi, T.: Public transport versus solo travel mode choices during the COVID-19 pandemic: Self-reported evidence from a developing country.Transportation Engineering, 5. (2021)

Abdullah, M., Ali, N., Aslam, A.B., Javid, M.A., Hussain, S.A.: Factors affecting the mode choice behavior before and during COVID-19 pandemic in Pakistan. Int. J. Transp. Sci. Technol. 11 , 174–186 (2022)

Article   Google Scholar  

Abu-Rayash, A., Dincer, I.: Analysis of Mobility Trends During the COVID-19 Coronavirus Pandemic: Exploring the Impacts on Global Aviation and Travel in Selected Cities, p. 68. Energy Research & Social Science (2020)

Advani, M., Sharma, N., Dhyani, R.: Mobility change in Delhi due to COVID and its’ immediate and long term impact on demand with intervened non-motorized transport friendly infrastructural policies. Transp. Policy. 111 , 28–37 (2021)

Aghabayk, K., Esmailpour, J., Shiwakoti, N.: Effects of COVID-19 on rail passengers’ crowding perceptions. Transp. Res. Part A. 154 , 186–202 (2021)

Google Scholar  

Ahangari, S., Chavis, C., Jeihani, M.: Public transit ridership analysis during the COVID-19 pandemic. MedRxiv preprint. Available at (2020). https://doi.org/10.1101/2020.10.25.20219105 (accessed on 3 February, 2021)

Airak, S., Sukor, N.S.A., Rahman, N.A.: Travel behaviour changes and risk perception during COVID-19: A case study of Malaysia.Transportation Research Interdisciplinary Perspectives,18. (2023)

Alama, M.J., Shahriera, H., Anika, M.A.H., Habib, M.A.: Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions. Transp. Lett. (2022). https://doi.org/10.1080/19427867.2022.2122110

Alidadi, M., Sharifi, A.: Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.Science of the Total Environment,850. (2022)

Almlof, E., Rubensson, I., Cebecauer, M., Jenelius, E.: Who continued traveling by public transport during COVID-19? Socioeconomic factors explaining travel behaviour in Stockholm 2020 based on smart-card data. European Transport Research Review, 13(31). (2021)

Aloi, A., Alonso, B., Benavente, J., Cordera, R., Echaniz, E., Gonzalez, F., Ladisa, C., Lezama-Romanelli, R., Lopez-Parra, A., Mazzei, V., Perrucci, L., Prieto-Quintana, D., Rodriguez, A., Sanudo, R.: Effects of the COVID-19 lockdown on urban mobility: Empirical evidence from the city of Santander (Spain).Sustainability, (2020). 12(9).

Anzai, A., Kobayashi, T., Linton, N.M., Kinoshita, R., Hayashi, K., Suzuki, A., Yang, Y., Jung, S., Miyama, T., Akhmetzhanov, A.R., Nishiura, H.: Assessing the impact of reduced travel on exportation dynamics of novel coronavirus infection (COVID-19). J. Clin. Med. 9 (2), 601 (2020)

Arellana, J., Marquez, L., Cantillo, V.: COVID-19 outbreak in Colombia: An analysis of its impacts on transport systems. Journal of Advanced Transportation, 2020. (2020)

Arimura, M., Ha, T.V., Okumura, K., Asada, T.: Changes in urban mobility in Sapporo city, Japan due to the Covid-19 emergency declarations.Transportation Research Interdisciplinary Perspectives,7. (2020)

Askitas, N., Tatsiramos, K., Verheyden: Lockdown strategies, mobility patterns and COVID-19. Discussion Paper Series, IZA DP No. 13293, Institute of Labor Economics. (2020)

Astroza, S., Tirachini, A., Hurtubia, R., Carrasco, J.A., Guevara, A., Munizaga, M., Figueroa, M., Torres, V.: Mobility changes, teleworking, and remote communication during the COVID-19 Pandemic in Chile. Transport Findings. Available at (2020). https://doi.org/10.32866/001c.13489 (accessed on 1 February, 2021)

Awad-Núñez, S., Julio, R., Gomez, J., Moya-Gómez, B., González, J.S.: Post-COVID-19 travel behaviour patterns: impact on the willingness to pay of users of public transport and shared mobility services in Spain.European Transport Research Review,13. (2021)

Badr, H.S., Du, H., Marshall, M., Dong, E., Squire, M.M., Gardner, L.M.: Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. Lancet Infect. Dis. 20 (11), 1247–1254 (2020)

Balbontin, C., Hensher, D.A., Beck, M.J., Giesen, R., Basnak, P., Vallejo-Borda, J.A., Venter, C.: Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia, South America and South Africa.Journal of Transport Geography,96. (2021)

Balbontin, C., Hensher, D.A., Beck, M.J.: Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia.Transportation Research Part E,162. (2022)

Basnak, P., Giesen, R., Mu˜noz, J.C.: Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile. Transp. Res. Part A. 159 , 140–156 (2022)

Beck, M.J., Hensher, D.A.: Insights into the impact of COVID-19 on household travel and activities in Australia – The early days of easing restrictions. Transp. Policy. 99 , 95–119 (2020a)

Beck, M.J., Hensher, D.A.: Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions. Transp. Policy. 99 , 76–93 (2020b)

Benita, F.: Human Mobility Behavior in COVID-19: A Systematic Literature Review and Bibliometric Analysis, p. 70. Sustainable Cities and Society (2021)

Bernardes, S.D., Bian, Z., Thambiran, S.S.M., Gao, J., Na, C., Zuo, F., Hudanich, N., Bhattacharyya, A., Ozbay, K., Iyer, S., Chow, J.Y.J., Nassif, H.: NYC recovery at a glance – The rise of buses and micromobility. C2 Smart White Paper Issue 4, arXiv preprint arXiv:2009.14019 (2020)

Bhaduri, E., Manoj, B.S., Wadud, Z., Goswami, A.K., Ghoudhury, C.F.: Modelling the Effects of COVID-19 on Travel mode Choice Behaviour in India, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Borkowski, P., Jazdzewska-Gutta, M., Szmelter-Jarosz, A.: Lockdowned: Everyday mobility changes in response to COVID-19.Journal of Transport Geography,90. (2021)

Borsati, M., Nocera, S., Percoco, M.: Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy. Res. Transp. Econ. (2022). https://doi.org/10.1016/j.retrec.2022.101194

Bounie, D., Camara, Y., Galbraith, J.W.: Consumers’ mobility, expenditure and online-offline substitution response to COVID-19: Evidence from French transaction data. Available at (2020). https://ssrn.com/abstract=3588373 (Accessed on 30 March, 2021)

Bucsky, P.: Modal Share Changes due to COVID-19: The case of Budapest, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Buhat, C.A.H., Lutero, D.S.M., Olave, Y.H., Torres, M.C., Rabajante, J.F.: Modeling the transmission of respiratory infectious diseases in mass transportation systems. Available at (2020). https://doi.org/10.1101/2020.06.09.20126334 (accessed on 12 January, 2021)

Campisi, T., Basbas, S., Skoufas, A., Akgun, N., Ticali, D., Tesoriere, G.: The impact of COVID-19 pandemic on the resilience of sustainable mobility in Sicily.Sustainability, 12(21). (2020)

Carteni, A., Francesco, L.D., Martino, M.: How Mobility Habits Influenced the Spread of the COVID-19 Pandemic: Results from the Italian case Study, p. 741. Science of the Total Environment (2020)

Carteni, A., Francesco, L.D., Martino, M.: The role of transport accessibility within the spread of the Coronavirus pandemic in Italy.Safety Science,133. (2021)

Chan, H.F., Skali, A., Savage, D.A., Stadelmann, D., Torgler, B.: Risk attitudes and human mobility during the COVID-19 pandemic.Scientific Reports,10. (2020)

Chen, Q., Pan, S.: Transport-related Experiences in China in Response to the Coronavirus (COVID-19), p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Chen, Z., Zhang, Q., Lu, Y., Guo, Z., Zhang, X., Zhang, W., Guo, C., Liao, C., Li, Q., Han, X., Lu, J.: Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chin. Med. J. 133 (9), 1044–1050 (2020)

Chen, C., Feng, T., Gu, X., Yao, B.: Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of the Netherlands. Transp. Policy. 117 , 98–107 (2022a)

Chen, Y., Sun, X., Deveci, M., Coffman, D.M.: The impact of the COVID-19 pandemic on the behaviour of bike sharing users.Sustainable Cities and Society,84. (2022b)

Chinazzi, M., Davis, J.T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Piontti, A.P., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M.E., Longini, I.M., Vespignani, A.: The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 368 , 395–400 (2020)

Cho, S.H., Park, H.C.: Exploring the behaviour change of crowding impedance on public transit due to COVID-19 pandemic: Before and after comparison. Transp. Lett. 13 , 367–374 (2021)

Choi, Y., Zou, L., Dresner, M.: The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants. Transp. Policy. 127 , 22–30 (2022)

Choi, S.E., Kim, J., Seo, D.: Travel patterns of free-floating e-bike-sharing users before and during COVID-19 pandemic.Cities,132. (2023)

Cintia, P., Fadda, D., Giannotti, F., Pappalardo, L., Rossetti, G., Rinzivillo, S., Bonato, P., Fabbri, F., Penone, F., Savarese, M., Checchi, D., Chiaromonte, F., Vineis, P., Guzzetta, G., Riccardo, F., Marziano, V., Poletti, P., Trentini, F., Bella, A., Andrianou, X., Manso, M., Fabiani, M., Bellino, S., Boros, S., Urdiales, A.M., Vescio, M.F., Brusaferro, S., Rezza, G., Pezzotti, P., Ajelli, M., Merler, S.: The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy. (2020). arXiv preprint ArXiv abs/2006.03141.

Ciuffini, F., Tengattini, S., Bigazzi, A.Y.: Mitigating increased driving after the COVID-19 pandemic: An analysis on mode share, travel demand, and public transport capacity.Transportation Research Record,1–14. (2021)

Cooley, P., Brown, S., Cajka, J., Chasteen, B., Ganapathi, L., Grefenstette, J., Hollingsworth, C.R., Lee, B.Y., Levine, B., Wheaton, W.D., Wagener, D.K.: The role of subway travel in an influenza epidemic: A New York City simulation. J. Urb. Health. 88 (5), 982–995 (2011)

Costa, C.S., Pitombo, C.S., Souza, F.L., Ud: Travel behavior before and during the COVID-19 pandemic in Brazil: Mobility changes and transport policies for a sustainable transportation system in the post-pandemic period.Sustainability, 14. (2022)

Couture, V., Dingel, J.I., Green, A., Handbury, J., Williams, K.R.: JUE Insight: Measuring movement and social contact with smartphone data: A real-time application to COVID-19.Journal of Urban Economics,127. (2022)

Cui, Z., Zhu, M., Wang, S., Wang, P., Zhou, Y., Cao, Q., Kopca, C., Wang, Y.: Traffic performance score for measuring the impact of covid-19 on urban mobility. arXiv preprint arXiv:2007.00648 (2020)

Currie, G., Jain, T., Aston, L.: Evidence of a post-COVID change in travel behaviour – self-reported expectations of commuting in Melbourne. Transp. Res. Part A. 153 , 218–234 (2021)

Dahlberg, M., Edin, P.-A., Gronqvist, E., Lyhagen, J., Osth, J., Siretskiy, A., Toger, M.: Effects of the COVID-19 pandemic on population mobility under mild policies: Causal evidence from Sweden. (2020). arXiv preprint arXiv:2004.09087.

Das, S., Boruah, A., Banerjee, A., Raoniar, R., Nama, S., Maurya, A.K.: Impact of COVID-19: A radical modal shift from public to private transport mode. Transp. Policy. 109 , 1–11 (2021)

Dasgupta, N., Funk, M.J., Lazard, A., White, B.E., Marshall, S.W.: Quantifying the social distancing privilege gap: A longitudinal study of smartphone movement. MedRxiv preprint. Available at (2020). https://doi.org/10.1101/2020.05.03.20084624 (accessed on 19 January, 2021)

De Haas, M., Faber, R., Hamersma, M.: How COVID-19 and the Dutch ‘intelligent Lockdown’ Change Activities, work and Travel Behavior: Evidence from Longitudinal data in the Netherlands, p. 6. Transportation Research Interdisciplinary Perspectives (2020)

De Vos, J.: The effect of COVID-19 and subsequent social distancing on travel behavior.Transportation Research Interdisciplinary Perspectives, 5. (2020)

Dingil, A.E., Esztergár-Kiss, D.: The influence of the Covid-19 pandemic on mobility patterns: The first wave’s results. Transp. Lett. 13 , 434–446 (2021)

Downey, L., Fonzone, A., Fountas, G., Semple, T.: The impact of COVID-19 on future public transport use in Scotland. Transp. Res. Part A. 163 , 338–352 (2022)

Echaniz, E., Rodríguez, A., Cordera, R., Benavente, J., Alonso, B., Sa˜nudo, R.: Behavioural changes in transport and future repercussions of the COVID-19 outbreak in Spain. Transp. Policy. 111 , 38–52 (2021)

Ecke, L., Magdolen, M., Chlond, B., Vortisch, P.: How the COVID-19 Pandemic Changes Daily Commuting routines – Insights from the German Mobility Panel, vol. 10, pp. 2175–2182. Case Studies on Transport Policy (2022)

Espinoza, B., Castillo-Chavez, C., Perrings, C.: Mobility restrictions for the control of epidemics: When do they work?PLoS ONE, 15(7). (2020)

Falchetta, G., Noussan, M.: The Impact of COVID-19 on transport demand, modal choices, and sectoral energy consumption in Europe. IAEE Energy Forum, May, 2020. (2020)

Fang, Y., Nie, Y., Penny, M.: Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data‐driven analysis.Journal of Medical Virology, 92(6). (2020a)

Fang, H., Wang, L., Yang, Y.: Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China.Journal of Public Economics,191. (2020b)

Fatmi, M.R.: COVID-19 impact on urban mobility. J. Urban Manage. 9 , 270–275 (2020)

Ferreira, S., Amorim, M., Lobo, A., Kern, M., Fanderl, N., Couto, A.: Travel mode preferences among german commuters over the course of COVID-19 pandemic. Transp. Policy. 126 , 55–64 (2022)

Fischedick, M.S., Shan, Y., Hubacek, K.: Implications of COVID-19 lockdowns on surface passenger mobility and related CO2 emission changes in Europe.Applied Energy,300. (2021)

Galeazzi, A., Cinelli, M., Bonaccorsi, G., Pierri, F., Schmidt, A.L., Scala, A., Pammolli, F., Quattrociocchi, W.: Human Mobility in Response to COVID-19 in France, Italy and UK, p. 11. Scientific Reports (2021)

Gao, J., Bernardes, S.D., Bian, Z., Ozbay, K., Iyer, S.: Initial impacts of COVID-19 on transportation systems: A case study of the U.S. epicenter, the New York Metropolitan Area. C2 Smart White Paper, arXiv preprint arXiv:2010.01168 (2020a)

Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J.: Mapping county-level mobility pattern changes in the United States in response to COVID-19. (2020b). arXiv preprint arXiv:2004.04544

Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Doepfer, D., Sethi, A.K., Reyes, J.F.M., Patz, J., Yandell, B.S.: Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic. arXiv preprint arXiv:2004.11430. (2020c)

Gao, J., Wang, J., Bian, Z., Bernardes, S.D., Chen, Y., Bhattacharyya, A., Thambiran, S.S.M., Ozbay, K., Iyer, S., Ban, X.J.: The effects of the COVID-19 pandemic on transportation systems in New York City and Seattle, USA. C2 Smart White Paper Issue 2, arXiv:2010.01170 (2020d)

Ghader, S., Zhao, J., Lee, M., Zhou, W., Zhao, G., Zhang, L.: Observed mobility behavior data reveal “social distancing inertia”. arXiv preprint arXiv:2004.14748 (accessed on 19 January, 2021). (2020)

Gkiotsalitis, K., Cats, O.: Public transport planning adaption under the COVID-19 pandemic crisis: Literature review of research needs and directions.Transport Reviews, 41(3). (2021)

Gkiotsalitis, K., Cats, O.: Optimal Frequency Setting of Metro Services in the age of COVID-19 Distancing Measures. Transport Science, Transportmetrica A (2022)

Book   Google Scholar  

Glaeser, E.L., Gorback, C., Redding, S.J.: JUE Insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities.Journal of Urban Economics,127. (2022)

Gonzalez, A.B.R., Wilby, M.R., Díaz, J.J.V., Pozo, R.F.: Characterization of COVID-19’s impact on mobility and short-term prediction of public transport demand in a mid-size city in Spain.Sensors,21. (2021)

Gramsch, B., Guevara, C.A., Munizaga, M., Schwartz, D., Tirachini, A.: The effect of dynamic lockdowns on public transport demand in times of COVID-19: Evidence from smartcard data. Transp. Policy. 126 , 136–150 (2022)

Guzman, L.A., Arellana, J., Oviedo, D., Aristiz´abal, C.A.M.: COVID-19, activity and mobility patterns in Bogot´a. are we ready for a ‘15-minute city’? Travel Behav. Soc. 24 , 245–256 (2021)

Habib, Y., Xia, E., Hashmi, S.H., Fareed, Z.: Non-linear spatial linkage between COVID-19 pandemic and mobility in ten countries: A lesson for future wave. J. Infect. Public Health. 14 , 1411–1426 (2021)

Hadjidemetriou, G.M., Sasidharan, M., Kouyialis, G., Parlikad, A.K.: The Impact of Government Measures and Human Mobility Trend on COVID-19 Related Deaths in the UK, p. 6. Transportation Research Interdisciplinary Perspectives (2020)

Harantová, V., Hájnik, A., Kalašová, A., Figlus, T.: The Effect of the COVID-19 Pandemic on Traffic flow Characteristics, Emissions Production and fuel Consumption at a Selected Intersection in Slovakia, vol. 15. Energies (2022)

Harrington, D.M., Hadjiconstantinou, M.: Changes in commuting behaviours in response to the COVID-19 pandemic in the UK.Journal of Transport & Health,24. (2022)

Hasselwander, M., Tamagusko, T., Bigotte, J.F., Ferreira, A., Mejia, A., Ferranti, E.J.S.: Building back Better: The COVID-19 Pandemic and Transport Policy Implications for a Developing Megacity, p. 69. Sustainable Cities and Society (2021)

Heiler, G., Reisch, T., Hurt, J., Forghani, M., Omani, A., Hanbury, A., Karimipour, F.: Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic. (2020). arXiv preprint arXiv:2008.10064

Hensher, D.A., Beck, M.J., Balbontin, C.: What does the quantum of working from home do to the value of commuting time used in transport appraisal? Transp. Res. Part A. 153 , 35–51 (2021)

Hensher, D.A., Balbontin, C., Beck, M.J., Wei, E.: The impact of working from home on modal commuting choice response during COVID-19: Implications for two metropolitan areas in Australia. Transp. Res. Part A. 155 , 179–201 (2022)

Hensher, D.A., Beck, M.J., Balbontin, C.: Working from home 22 months on from the beginning of COVID-19: What have we learned for future provision of transport services?Research in Transportation Economics,98. (2023)

Heydari, S., Konstantinoudis, G., Behsoodi, A.W.: Effect of the COVID-19 Pandemic on bike-sharing Demand and hire time: Evidence from Santander Cycles in London, p. 16. PLoS ONE (2021)

Hintermann, B., Schoeman, B., Molloy, J., Schatzmann, T., Tchervenkov, C., Axhausen, K.W.: The impact of COVID-19 on mobility choices in Switzerland.Transportation Research Part A,169. (2023)

Hotle, S., Murray-Tuite, P., Singh, K.: Influenza risk Perception and travel-related Health Protection Behavior in the US: Insights for the Aftermath of the COVID-19 Outbreak, vol. 5. Transportation Research Interdisciplinary Perspectives (2020)

Huang, Z., Loo, B.P.Y., Axhausen, K.W.: Travel behaviour changes under work-from-home (WFH) arrangements during COVID-19. Travel Behav. Soc. 30 , 202–211 (2023)

Iacus, S.M., Natale, F., Santamaria, C., Spyratos, S., Vespe, M.: Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact.Safety Science,129. (2020a)

Iacus, S.M., Santamaria, C., Sermi, F., Spyratos, S., Tarchi, D., Vespe, M.: Human mobility and COVID-19 initial dynamics. Nonlinear Dyn. 101 , 1901–1919 (2020b)

Jaekel, B., Muley, D.: Transport Impacts in Germany and State of Qatar: An Assessment During the First wave of COVID-19, p. 13. Transportation Research Interdisciplinary Perspectives (2022)

Javadinasr, M., Maggasy, T., Mohammadi, M., Mohammadain, K., Rahimi, E., Salon, D., Conway, M.W., Pendyala, R., Derrible, S.: The long-term effects of COVID-19 on travel behavior in the United States: A panel study on work from home, mode choice, online shopping, and air travel. Transp. Res. Part F. 90 , 466–484 (2022)

Jenelius, E., Cebecauer, M.: Impacts of COVID-19 on Public Transport Ridership in Sweden: Analysis of Ticket Validations, Sales and Passenger Counts, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Jia, J.S., Yuan, Y., Xu, G., Jia, J., Christakis, N.A.: Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 582 , 389–394 (2020)

Jiang, S., Cai, C.: Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China. Transp. Policy. 127 , 158–170 (2022)

Jiao, J., Azimian, A.: Exploring the factors affecting travel behaviors during the second phase of the COVID-19 pandemic in the United States. Transp. Lett. 13 , 331–343 (2021)

Jou, R.-C., Yeh, C.-S., Chen, K.-H.: Travel Behavior Changes after COVID-19 Outbreak in Taiwan. Journal of Advanced Transportation, 2022. (2022)

Kalter, M.J.O., Geurs, K.T., Wismans, L.: Post COVID-19 teleworking and car use intentions. evidence from large scale GPS-tracking and survey data in the Netherlands.Transportation Research Interdisciplinary Perspectives, 12. (2021)

Kartal, M.T., Depren, O., Depren, S.K.: The Relationship Between Mobility and COVID-19 Pandemic: Daily Evidence from an Emerging Country by Causality Analysis, p. 10. Transportation Research Interdisciplinary Perspectives (2021)

Kaufman, S.M., Moss, M.L., Mcguinness, K.B., Cowan, N.R., Rudner, C.E., Olivia, L., Jenee, M., Josh, K., Katherine, R., Rachel, W.: Transportation during Coronavirus in New York City. Rudin Center for Transportation Policy & Management, NYU. Available at (2020). https://wagner.nyu.edu/impact/research/publications/transportation-during-coronavirus-nyc (accessed on 12 August, 2020)

Khan, K.S., Kunz, R., Kleijnen, J., Antes, G.: Five steps to conducting a systematic review. J. R. Soc. Med. 96 , 118–121 (2003)

Kim, K.: Impacts of COVID-19 on Transportation: Summary and Synthesis of Interdisciplinary Research, p. 9. Transportation Research Interdisciplinary Perspectives (2021)

Kissler, S.M., Kishore, N., prabhu, M., Goffman, D., Beilin, Y., Landau, R., Gyamfi-Bannerman, C., Bateman, B.T., Snyder, J., Razavi, A.S., Katz, D., Gal, J., Bianco, A., Stone, J., Larremore, D., Buckee, C.O., Grad, Y.H.: Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City.Nature Communications, 11(4674), (2020)

Klein, B., LaRock, T., McCabe, S., Torres, L., Friedland, L., Privitera, F., Lake, B., Kraemer, M.U.G., Brownstein, J.S., Lazer, D., Eliassi-Rad, T., Scarpino, S.V., Vespignani, A., Chinazzi, M.: Reshaping a nation: Mobility, commuting, and contact patterns during the COVID-19 outbreak. Available at (2020a). https://www.networkscienceinstitute.org/publications/reshaping-a-nation-mobility-commuting-and-contact-patterns-during-the-covid-19-outbreak (accessed on 17 February, 2021)

Klein, B., LaRock, T., McCabe, S., Torres, L., Privitera, F., Lake, B., Kraemer, M.U.G., Brownstein, J.S., Lazer, D., Eliassi-Rad, T., Scarpino, S.V., Chinazzi, M., Vespignani, A.: Assessing changes in commuting and individual mobility in major metropolitan areas in the United States during the COVID-19 outbreak. Available at (2020b). https://www.networkscienceinstitute.org/publications/assessing-changes-in-commuting-and-individual-mobility-in-major-metropolitan-areas-in-the-united-states-during-the-covid-19-outbreak (accessed on 7 January, 2021)

Kłos-Adamkiewicz, Z., Gutowski, P.: The Outbreak of COVID-19 Pandemic in Relation to Sense of Safety and Mobility Changes in Public Transport Using the Example of Warsaw, vol. 14. Sustainability (2022)

Konecny, V., Brídziková, M., Senko, Å.: Impact of COVID-19 and anti-pandemic Measures on the Sustainability of Demand in Suburban bus Transport. The case of the Slovak Republic, p. 13. Sustainability (2021)

Kraemer, M.U.G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D.M., du Plessis, L., Faria, N.R., Li, R., Hanage, W.P., Brownstein, J.S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O.G., Scarpino, S.V.: The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 368 (6490), 493–497 (2020)

Kubal’ák, S., Kalašová, A., Hájnik, A.: The bike-sharing system in slovakia and the Impact of COVID-19 on this shared mobility service in a selected city.Sustainability, 13. (2021)

Lau, H., Khosrawipour, V., Kocbach, P., Mikolajczyk, A., Ichii, H., Zacharski, M., Bania, J., Khosrawipour, T.: The association between international and domestic air traffic and the coronavirus (COVID-19) outbreak. J. Microbiol. Immunol. Infect. 53 , 467–472 (2020)

Lee, H., Park, S.J., Lee, G.R., Kim, J.E., Lee, J.H., Jung, Y., Nam, E.W.: The relationship between trends in COVID-19 prevalence and traffic levels in South Korea. Int. J. Infect. Dis. 96 , 399–407 (2020a)

Lee, M., Zhao, J., Sun, Q., Pan, Y., Zhou, W., Xiong, C., Zhang, L.: Human mobility trends during the early stage of the COVID-19 pandemic in the United States.PLoS ONE, 15(11). (2020b)

Lee, S., Ko, E., Jang, K., Kim, S.: Understanding individual-level travel behavior changes due to COVID-19: Trip frequency, trip regularity, and trip distance.Cities,135. (2023)

Li, T., Wang, J., Huang, J., Yang, W., Chen, Z.: Exploring the dynamic impacts of COVID-19 on intercity travel in China.Journal of Transport Geography,95. (2021a)

Li, H., Zhang, Y., Zhu, M., Ren, G.: Impacts of COVID-19 on the usage of public bicycle share in London. Transp. Res. Part A. 150 , 140–155 (2021b)

Li, A., Zhao, P., Haitao, H., Mansourian, A., Axhausen, K.W.: How did micro-mobility Change in Response to COVID-19 Pandemic? A case Study Based on spatial-temporal-semantic Analytics, p. 90. Computers, Environment and Urban Systems (2021c)

Li, A., Zhao, P., He, H., Axhause, K.W.: Understanding the variations of micro-mobility behavior before and during COVID-19 pandemic period. Transportation Research Board 100th Annual Meeting, Washington, DC., USA. (2021d)

Limsawasd, C., Athigakunagorn, N., Khathawatcharakun, P., Boonmee, A.: Skip-stop strategy patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak. Transp. Policy. 126 , 225–238 (2022)

Linka, K., Peirlinck, M., Costabal, F.S., Kuhl, E.: Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions. Comput. Methods Biomech. BioMed. Eng. 23 (11), 710–717 (2020)

Liu, S., Yamamoto, T.: Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan. Transp. Res. Part A. 159 , 1–16 (2022)

Liu, K., Ai, S., Song, S., Zhu, G., Tian, F., Li, H., Gao, Y., Wu, Y., Zhang, S., Shao, Z., Liu, Q., Lin, H.: Population movement, city closure in Wuhan, and geographical expansion of the COVID-19 infection in China in January 2020. Clin. Infect. Dis. 71 (16), 2045–2051 (2020a)

Liu, H., Bai, X., Shen, H., Pang, X., Liang, Z., Liu, Y.: Synchronized travel restrictions across cities can be effective in COVID-19 control. MedRxiv preprint. Available at (2020b). https://doi.org/10.1101/2020.04.02.20050781 (accessed on 16 February, 2021)

Liu, L., Miller, H.J., Scheff, J.: The impacts of COVID-19 pandemic on public transit demand in the United States. PLos One, 15(11), (2020c). https://doi.org/10.1371/journal.pone.0242476 (accessed on 16 March, 2021)

Liu, X., Kortoçi, P., Motlagh, N.H., Nurmi, P., Tarkoma, S.: A Survey of COVID-19 in Public Transportation: Transmission risk, Mitigation and Prevention, p. 1. Multimodal Transportation (2022)

Llaguno-Munitxa, M., Bou-Zeid, E.: Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment.Transportation Research Part D,115. (2023)

Loo, B.P.Y., Huang, Z.: Spatio-temporal Variations of Traffic Congestion Under work from home (WFH) Arrangements: Lessons Learned from COVID-19, p. 124. Cities (2022)

Lozzi, G., Rodrigues, M., Marcucci, E., Teoh, T., Gatta, V., Pacelli, V.: COVID-19 and Urban Mobility: Impacts and Perspectives. Research for TRAN Committee. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels (2020)

Lu, J., Lin, A., Jiang, C., Zhang, A., Yang, Z.: Influence of transportation network on transmission heterogeneity of COVID-19 in China.Transportation Research Part C,129. (2021)

Mancinelli, E., Rizza, U., Canestrari, F., Graziani, A., Virgili, S., Passerini, G.: New habits of travellers deriving from COVID-19 pandemic: A survey in ports and airports of the adriatic region.Sustainability, 14. (2022)

Manzira, C.K., Charly, A., Caulfield, B.: Assessing the Impact of Mobility on the Incidence of COVID-19 in Dublin City, p. 80. Sustainable Cities and Society (2022)

Marra, A.D., Sun, L., Corman, F.: The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area. Transp. Policy. 116 , 258–268 (2022)

Mars, L., Arroyo, R., Ruiz, T.: Mobility and wellbeing during the covid-19 lockdown. Evidence from Spain. Transp. Res. Part A. 161 , 107–129 (2022)

Martin-Calvo, D., Aleta, A., Pentland, A., Moreno, Y., Moro, E.: Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. MIT Connection Science. Available at (2020). https://connection.mit.edu/sites/default/files/publication-pdfs/Preliminary_Report_Effectiveness_of_social_distance_strategies_COVID-19%20(1).pdf (accessed on 16 February, 2021)

Mashrur, S.M., Wang, K., Habib, K.N.: Will COVID-19 be the end for the public transit? Investigating the impacts of public health crisis on transit mode choice. Transp. Res. Part A. 164 , 352–378 (2022)

Medlock, K.B., Temzelides, T., Hung, S.Y.: COVID-19 and the value of safe transport in the United States.Scientific Reports,11. (2021)

Meena, S.: Impact of novel coronavirus (COVID-19) pandemic on travel pattern: A case study of India. Indian J. Sci. Technol. 13 (24), 2491–2501 (2020)

Meister, A., Mondal, A., Asmussen, K.E., Bhat, C., Axhausen, K.W.: Modeling urban mode choice behavior during the COVID-19 pandemic in switzerland using mixed multiple discrete-continuous extreme value models.Transportation Research Record,1–12. (2022)

Mogaji, E.: Impact of COVID-19 on Transportation in Lagos, Nigeria, p. 6. Transportation Research Interdisciplinary Perspectives (2020)

Mollers, A., Specht, S., Wessel, J.: The impact of the Covid-19 pandemic and government intervention on active mobility. Transp. Res. Part A. 165 , 356–375 (2022)

Morita, H., Nakamura, S., Hayashi, Y.: Changes of urban activities and behaviors due to COVID-19 in Japan. Available at (2020). https://ssrn.com/abstract=3594054 (accessed on 3 February, 2021)

Moslem, S., Campisi, T., Szmelter-Jarosz, A., Duleba, S., Nahiduzzaman, K.M., Tesoriere, G.: Best-worst method for modelling mobility choice after COVID-19: Evidence from Italy. Sustainability. 12 (17), 6824 (2020)

Mouratidis, K., Peters, S.: COVID-19 impact on teleactivities: Role of built environment and implications for mobility. Transp. Res. Part A. 158 , 251–270 (2022)

Muley, D., Shahin, M., Dias, C., Abdullah, M.: Role of transport during outbreak of infectious diseases: Evidence from the past. Sustainability. 12 (18), 7367 (2020)

Muller, S.A., Balmer, M., Neumann, A., Nagel, K.: Mobility traces and spreading of COVID-19. MedRxiv preprint. Available at https://doi.org/ (2020). https://doi.org/10.1101/2020.03.27.20045302 (accessed on 25 January, 2021)

Musselwhite, C., Avineri, E., Susilo, Y.: Editorial JTH 16 – The Coronavirus disease COVID-19 and implications for transport and health.Journal of Transport & Health,16. (2020)

Mussone, L., Changizi, F.: A Study on the Factors that Influenced the Choice of Transport mode Before, During, and After the First Lockdown in Milan, Italy, p. 136. Cities (2023)

Navarrete-Hernandez, P., Rennert, L., Balducci, A.: An evaluation of the impact of COVID-19 safety measures in public transit spaces on riders’ worry of virus contraction. Transp. Policy. 131 , 1–12 (2023)

Nian, G., Peng, B., Sun, D., Ma, W., Peng, B., Huang, T.: Impact of COVID-19 on urban mobility during post-epidemic period in Megacities: From the perspectives of taxi travel and social vitality. Sustainability, 12(19). (2020)

Nikiforiadis, A., Mitropoulos, L., Kopelias, P., Basbas, S., Stamatiadis, N., Kroustali, S.: Exploring mobility pattern changes between before, during and after COVID-19 lockdown periods for young adults.Cities,125. (2022)

Nikolaidou, A., Kopsacheilis, A., Georgiadis, G., Noutsias, T., Politis, I., Fyrogenis: Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis.Cities,134. (2023)

Oestreich, L., Rhoden, P.S., Vieira, J.S., Ruiz-Padillo, A.: Impacts of the COVID-19 pandemic on the profile and preferences of urban mobility in Brazil: Challenges and opportunities. Travel Behav. Soc. 31 , 312–322 (2023)

Orro, A., Novales, M., Monteagudo, A., Perez-Lopez, J.-B., Bugarin, M.R.: Impact on city bus transit services of the COVID–19 lockdown and return to the New Normal: The case of a Coruña (Spain).Sustainability, 12(17). (2020)

Oum, T.H., Wang, K.: Socially optimal lockdown and travel restrictions for fighting communicable virus including COVID-19. Transp. Policy. 96 , 94–100 (2020)

Oztig, L.I., Askin, O.E.: Human mobility and coronavirus disease 2019 (COVID-19): A negative binomial regression analysis. Public. Health. 185 , 364–367 (2020)

Pan, Y., He, S.Y.: Analyzing COVID-19’s impact on the travel mobility of various social groups in China’s Greater Bay Area via mobile phone big data. Transp. Res. Part A. 159 , 263–281 (2022)

Pan, Y., Darzi, A., Kabiri, A., Zhao, G., Luo, W., Xiong, C., Zhang, L.: Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States.Scientific Reports,10. (2020)

Pang, J., He, Y., Shen, S.: High-speed railways and the spread of Covid-19. Travel Behav. Soc. 30 , 1–10 (2023)

Parady, G., Taniguchi, A., Takami, K.: Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing the effects of risk perception and social influence on going-out self-restriction.Transportation Research Interdisciplinary Perspectives,7. (2020)

Park, J.: Changes in subway ridership in response to COVID-19 in Seoul, South Korea: Implications for social distancing.Cureus, 12(4). (2020)

Parr, S., Wolshon, B., Renne, J., Murray-Tuite, P., Kim, K.: Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction.Natural Hazards Review, 21(3). (2020)

Pawar, D.S., Yadav, A.K., Akolekar, N., Velaga, N.R.: Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown.Transportation Research Interdisciplinary Perspectives,7. (2020)

Peng, Y., Lopez, J.M.R., Santos, A.P., Mobeen, M., Scheffran, J.: Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil, Cities, 134. (2023)

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., Tizzoni, M.: COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown.Scientific Data, 7(230). (2020)

Peralvo, F.C., Vanegas, P.C., Ord´o˜nez, E.A.: A systematic review of COVID-19 transport policies and mitigation strategies around the globe.Transportation Research Interdisciplinary Perspectives,15. (2022)

Pozo, R.F., Wilby, M.R., Díaz, J.J.V., Gonz´alez, A.B.R.: Data-driven analysis of the impact of COVID-19 on Madrid’s public transport during each phase of the pandemic.Cities,127. (2022)

Przybylowski, A., Stelmak, S., Suchanek, M.: Mobility behaviour in view of the impact of the COVID-19 pandemic – Public transport users in Gdansk case study.Sustainability, 13(1). (2021)

Pullano, G., Valdano, E., Scarpa, N., Rubrichi, S., Colizza, V.: Population mobility reductions during COVID-19 epidemic in France under lockdown. MedRxiv preprint. Available at (2020). https://doi.org/10.1101/2020.05.29.20097097 (accessed on 8 January, 2021)

Rasca, S., Markvica, K., Ivanschitz, B.P.: Impacts of COVID-19 and Pandemic Control Measures on Public Transport Ridership in European Urban areas – The Cases of Vienna, Innsbruck, Oslo, and Agder, p. 10. Transportation Research Interdisciplinary Perspectives (2021)

Rosik, P., Komornicki, T., Duma, P., Goliszek, S.: The effect of border closure on road potential accessibility in the regions of the EU-27. The case of the COVID-19 pandemic. Transp. Policy. 126 , 188–198 (2022)

Rothengatter, W., Zhang, J., Hayashi, Y., Nosach, A., Wang, K., Oum, T.H.: Pandemic waves and the time after Covid-19 – consequences for the transport sector. Transp. Policy. 110 , 225–237 (2021)

Ruiz-Euler, A., Privitera, F., Giuffrida, D., Lake, B., Zara, I.: Mobility patterns and income distribution in times of crisis: U.S. urban centers during the COVID-19 Pandemic. Available at (2020). https://ssrn.com/abstract=3572324 (accessed on 14 January, 2021)

Saladie, O., Bustamante, E., Gutierrez, A.: COVID-19 Lockdown and Reduction of Traffic Accidents in Tarragona Province, Spain, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Sangveraphunsiri, T., Fukushige, T., Jongwiriyanurak, N., Tanaksaranond, G., Jarumaneeroj, P.: Impacts of the COVID-19 Pandemic on the spatio-temporal Characteristics of a bicycle-sharing System: A case Study of Pun Pun, p. 17. PLoS ONE, Bangkok, Thailand (2022)

Santamaria, C., Sermi, F., Spyratos, S., Iacus, S.M., Annunziato, A., Tarchi, D., Vespe, M.: Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data: A European regional analysis.Safety Science,132. (2020)

Sasidharan, M., Singh, A., Torbaghan, M.E., Parlikad, A.K.: A vulnerability-based Approach to human-mobility Reduction for Countering COVID-19 Transmission in London While Considering Local air Quality, p. 741. Science of the Total Environment (2020)

Schaefer, K.J., Tuitjer, L., Keitel, M.L.: Transport disrupted – substituting public transport by bike or car under Covid 19. Transp. Res. Part A. 153 , 202–217 (2021)

Schlosser, F., Maier, B.F., Jack, O., Hinrichs, D., Zachariae, A., Brockmann, D.: COVID-19 lockdown induces disease-mitigating structural changes in mobility networks.PNAS, Proceedings of the National Academy of Sciences of the United States of America, 117(52). (2020)

Schwartz, S.: Global mobility response to COVID-19: How cities are responding, recovering, and reopening transportation systems around the world. Available at (2020a). https://www.samschwartz.com/staff-reflections/2020/6/globalresponse (accessed on 6 October, 2020)

Schwartz, S.: Public Transit and COVID-19 Pandemic: Global Research and best Practices. American Public Transportation Association, Washington, DC. USA (2020b)

Shaheen, S., Wong, S.: Public transit and shared mobility COVID-19 recovery: Policy recommendations and research needs. University of California. Available at (2020). https://escholarship.org/uc/item/9nh6w2gq (accessed on 13 January, 2021)

Shakibaei, S., de Jong, G.C., Alpkokin, P., Rashidi, T.H.: Impact of the COVID-19 Pandemic on Travel Behavior in Istanbul: A Panel data Analysis, p. 65. Sustainable Cities and Society (2020)

Shamshiripour, A., Rahimi, E., Shabanpour, R., Mohammadian, A.: How is COVID-19 Reshaping activity-travel Behavior? Evidence from a Comprehensive Survey in Chicago, p. 7. Transportation Research Interdisciplinary Perspectives (2020)

Shelat, S., Cats, O., Cranenburgh, S.: Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands. Transp. Res. Part A. 159 , 357–371 (2022)

Shi, Z., Fang, Y.: Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China. MedRxiv preprint. Available at (2020). https://doi.org/10.1101/2020.03.15.20034199 (accessed on 22 March, 2021)

Simovi´c, S., Ivaniševi´c, T., Bradic´, B., Cˇ icˇevic´, S., Trifunovic´, A.: What causes changes in passenger behavior in South-East Europe during the COVID-19 pandemic? Sustainability,13. (2021)

Snyder, H.: Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 104 , 333–339 (2019)

Sobieralski, J.B.: COVID-19 and airline employment: Insights from historical uncertainty shocks to the industry.Transportation Research Interdisciplinary Perspectives, 5. (2020)

Sogbe, E.: The evolving impact of coronavirus (COVID-19) pandemic on public transportation in Ghana. Case Stud. Transp. Policy. 9 , 1607–1614 (2021)

Sokadjo, Y.M., Atchade, M.N.: The Influence of Passenger air Traffic on the Spread of COVID-19 in the World, p. 8. Transportation Research Interdisciplinary Perspectives (2020)

Song, J., Zhang, L., Qin, Z., Ramli, M.A.: Spatiotemporal evolving patterns of bike-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak.Physica A,592. (2022)

Su, M., Hu, B., Jiang, Y., Zhang, Z., Li, Z.: Relationship between the Chinese main air transport network and COVID-19 pandemic transmission.Mathematics,10. (2022a)

Su, M., Hu, B., Luan, W., Tian, C.: Effects of COVID-19 on China’s civil aviation passenger transport market. Res. Transp. Econ. (2022b). https://doi.org/10.1016/j.retrec.2022.101217

Suman, H.K., Agarwal, A., Bolia, N.B.: Public transport operations after lockdown: How to make it happen? Trans. Indian Natl. Acad. Eng. 5 , 149–156 (2020)

Sung, H.: Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic.Cities,131. (2022)

Sy, K.T.L., Martinez, M.E., Rader, B., White, L.F.: Socioeconomic Disparities in Subway use and COVID-19 Outcomes in New York City. American Journal of Epidemiology (2020)

Szczepanek, W.K., Kruszyna, M.: The Impact of COVID-19 on the Choice of Transport Means in Journeys to work Based on the Selected Example from Poland, vol. 14. Sustainability (2022)

Tan, L., Ma, C.: Choice behavior of commuters’ rail transit mode during the COVID-19 pandemic based on logistic model. J. Traffic Transp. Eng. (English Edition). 8 (2), 186–195 (2021)

Teixeira, J.F., Lopes, M.: The link Between bike Sharing and Subway use During the COVID-19 Pandemic: The case-study of New York’s Citi Bike, p. 6. Transportation Research Interdisciplinary Perspectives (2020)

Teixeira, J.F., Silva, C., Moura, S´a, F.: The strengths and weaknesses of bike sharing as an alternative mode during disruptive public health crisis: A qualitative analysis on the users’ motivations during COVID-19. Transp. Policy. 129 , 24–37 (2022)

Tiikkaja, H., Viri, R.: The Effects of COVID-19 Epidemic on Public Transport Ridership and Frequencies. A case Study from Tampere, Finland, p. 10. Transportation Research Interdisciplinary Perspectives (2021)

Tirachini, A., Cats, O.: COVID-19 and public transportation: Current assessment, prospects, and research needs. J. Public Transp. 22 (1), 1–21 (2020)

Vannoni, M., McKee, M., Semenza, J.C., Bonell, C., Stuckler, D.: Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: A cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020.Globalization and Health,16. (2020)

Vichiensan, V., Hayashi, Y., Kamnerdsap, S.: COVID-19 countermeasures and passengers’ confidence of urban rail travel in Bangkok.Sustainability, 13. (2021)

Vickerman, R.: Will Covid-19 put the public back in public transport? A UK perspective. Transp. Policy. 103 , 95–102 (2021)

Wang, D., Zuo, F., Gao, J., He, Y., Bian, Z., Bernardes, S.D., Na, C., Wang, J., Petinos, J., Ozbay, K., Chow, J.Y.J., Iyer, S., Nassif, H., Ban, X.J.: Agent-based simulation model and deep learning techniques to evaluate and predict transportation trends around COVID-19. C2 Smart White Paper Issue 3, https://arxiv.org/abs/2010.09648 (2020)

Wang, Y., Wang, Z., Wang, J., Li, M., Wang, S., He, X., Zhou, C.: Evolution and control of the COVID-19 pandemic: A global perspective.Cities,130. (2022)

Wei, Y., Wang, J., Song, W., Xiu, C., Ma, L., Pei, T.: Spread of COVID-19 in China: Analysis from a city-based epidemic and mobility model.Cities,110. (2021)

Wellenius, G.A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T.C., Hennessy, J., Williams, B., Gadepalli, K., Boulanger, A., Pearce, A., Kamath, C., Schlosberg, A., Bendebury, C., Stanton, C., Bavadekar, S., Pluntke, C., Desfontaines, D., Jacobson, B., Armstrong, Z., Gipson, B., Wilson, R., Widdowson, A., Chou, K., Oplinger, A., Shekel, T., Jha, A.K., Gabrilovich, E.: Impacts of social distancing policies on mobility and COVID-19 case growth in the US.Nature Communications,12. (2021)

Wen, L., Sheng, M., Sharp, B.: The Impact of COVID-19 on Changes in Community Mobility and Variation in Transport Modes. New Zealand Economic Papers (2021)

WHO:. Coronavirus. World Health Organization. Available at (2021). https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed on 17 March, 2021)

Wielechowski, M., Czech, K., Grzeda, L.: Decline in mobility: Public transport in Poland in the time of the COVID-19 pandemic. Economies. 8 (4), 78 (2020)

Wilbur, M., Ayman, A., Ouyang, A., Poon, V., Kabir, R., Vadali, A., Pugliese, P., Freudberg, D., Laszka, A., Dubey, A.: Impact of COVID-19 on public transit accessibility and ridership. arXiv preprint arXiv:2008.02413. (2020)

Williams, C., Schweiger, B., Diner, G., Gerlach, F., Haaman, F., Krause, G., Nienhaus, A., Buchholz, U.: Seasonal influenza risk in hospital healthcare workers is more strongly associated with household than occupational exposures: results from a prospective cohort study in Berlin, Germany, 2006/07.BMC Infectious Diseases, 10(8). (2010)

Wolfswinkel, J.F., Furtmueller, E., Wilderom, C.P.M.: Using grounded theory as a method for rigorously reviewing literature. Eur. J. Inform. Syst. 22 (1), 45–55 (2013)

Xu, P., Dredze, M., Broniatowski, D.A.: The twitter social mobility index: Measuring social distancing practices from geolocated tweets.Journal of Medical Internet Research, 22(12). (2020)

Yabe, T., Tsubouchi, K., Fujiwara, N., Wada, T., Sekimoto, Y., Ukkusuri, S.V.: Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.Scientific Reports,10. (2020)

Yang, S., Chen, Z.: The impact of COVID-19 on high-speed rail and aviation operations.Sustainability, 14. (2022)

Yang, Y., Cao, M., Cheng, L., Zhai, K., Zhao, X., Vos, J.D.: Exploring the Relationship Between the COVID-19 Pandemic and Changes in Travel Behaviour: A Qualitative Study, p. 11. Transportation Research Interdisciplinary Perspectives (2021)

Yilmazkuday, H.: COVID-19 spread and inter-county travel: Daily evidence from the U.S. Transportation Research Interdisciplinary Perspectives, 8. (2020)

Yuksel, M., Aydede, Y., Begolli, F.: Dynamics of social mobility during the COVID-19 pandemic in Canada. Discussion Paper Series, IZA DP No. 13376, Institute of Labor Economics. (2020)

Zavareh, M.F., Mehdizadeh, M., Nordfjærn, T.: Demand for mitigating the risk of COVID-19 infection in public transport: The role of social trust and fatalistic beliefs. Transp. Res. Part F: Psychol. Behav. 84 , 348–362 (2022)

Zhang, X., Ji, Z., Zheng, Y., Ye, X., Li, D.: Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models.Cities,107. (2020a)

Zhang, Y., Zhang, A., Wang, J.: Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China. Transp. Policy. 94 , 34–42 (2020b)

Zhang, L., Ghader, S., Pack, M.L., Xiong, C., Darzi, A., Yang, M., Sun, Q., Kabiri, A., Hu, S.: An interactive COVID-19 mobility impact and social distancing analysis platform. Transportation Research Board 100th Annual Meeting, Washington, DC., USA. (2021a)

Zhang, J., Hayashi, Y., Frank, L.D.: COVID-19 and transport: Findings from a world-wide expert survey. Transp. Policy. 103 , 68–85 (2021b)

Zhang, J., Zhang, R., Ding, H., Li, S., Liu, R., Ma, S., Zhai, B., Kashima, S., Hayashi, Y.: Effects of transport-related COVID-19 policy measures: A case study of six developed countries. Transp. Policy. 110 , 37–57 (2021c)

Zhao, S., Zhuang, Z., Cao, P., Ran, J., Gao, D., Lou, Y., Yang, L., Cai, Y., Wang, W., He, D., Wang, M.H.: Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: A correlational analysis. J. Travel Med. 27 (2), 1–3 (2020a)

Zhao, S., Zhuang, Z., Ran, J., Lin, J., Yang, G., Yang, L., He, D.: The Association Between Domestic Train Transportation and Novel Coronavirus (2019-nCoV) Outbreak in China from 2019 to 2020: A data-driven Correlational Report, p. 33. Travel Medicine and Infectious Disease (2020b)

Zheng, R., Xu, Y., Wang, W., Ning, G., Bi, Y.: Spatial Transmission of COVID-19 via Public and Private Transportation in China, p. 34. Travel Medicine and Infectious Disease (2020)

Zhou, K., Hu, D., Li, F.: Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data. Transp. Policy. 125 , 164–178 (2022)

Zhu, P., Guo, Y.: The role of high-speed rail and air Travel in the Spread of COVID-19 in China, p. 42. Travel Medicine and Infectious Disease (2021)

Zubair, H., Karoonsoontawong, A., Kanitpong, K.: Effects of COVID-19 on Travel Behavior and mode Choice: A case Study for the Bangkok Metropolitan Area, p. 14. Sustainability (2022)

Zuo, F., Wang, J., Gao, J., Ozbay, K., Ban, X.J., Shen, Y., Yang, H., Iyer, S.: An interactive data visualization and analytics tool to evaluate mobility and sociability trends during COVID-19. In San Diego ’20: The 9th SIGKDD International Workshop for Urban Computing, August 24, 2020, San Diego, CA. ACM, New York, NY, USA. (2020)

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Acknowledgements

This research was supported by a grant from R&D Program (PK2202C2) of the Korea Railroad Research Institute, Republic of Korea.

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Lee, KS., Eom, J.K. Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility. Transportation 51 , 1907–1961 (2024). https://doi.org/10.1007/s11116-023-10392-2

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Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

• Israel Júnior Borges do Nascimento 1 , 2 ,
• Dónal P. O’Mathúna 3 , 4 ,
• Thilo Caspar von Groote 5 ,
• Hebatullah Mohamed Abdulazeem 6 ,
• Ishanka Weerasekara 7 , 8 ,
• Ana Marusic 9 ,
• Livia Puljak   ORCID: orcid.org/0000-0002-8467-6061 10 ,
• Vinicius Tassoni Civile 11 ,
• Irena Zakarija-Grkovic 9 ,
• Tina Poklepovic Pericic 9 ,
• Alvaro Nagib Atallah 11 ,
• Santino Filoso 12 ,
• Nicola Luigi Bragazzi 13 &
• Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume  21 , Article number:  525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

World Health Organization. Timeline - COVID-19: Available at: https://www.who.int/news/item/29-06-2020-covidtimeline . Accessed 1 June 2021.

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available at: https://coronavirus.jhu.edu/map.html . Accessed 1 June 2021.

Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, et al. Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19). J Clin Med. 2020;9(2):601.

Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395–400. https://doi.org/10.1126/science.aba9757 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fidahic M, Nujic D, Runjic R, Civljak M, Markotic F, Lovric Makaric Z, et al. Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19. BMC Med Res Methodol. 2020;20(1):161. https://doi.org/10.1186/s12874-020-01047-2 .

EPPI Centre . COVID-19: a living systematic map of the evidence. Available at: http://eppi.ioe.ac.uk/cms/Projects/DepartmentofHealthandSocialCare/Publishedreviews/COVID-19Livingsystematicmapoftheevidence/tabid/3765/Default.aspx . Accessed 1 June 2021.

NCBI SARS-CoV-2 Resources. Available at: https://www.ncbi.nlm.nih.gov/sars-cov-2/ . Accessed 1 June 2021.

Gustot T. Quality and reproducibility during the COVID-19 pandemic. JHEP Rep. 2020;2(4):100141. https://doi.org/10.1016/j.jhepr.2020.100141 .

Article   PubMed   PubMed Central   Google Scholar  

Kodvanj, I., et al., Publishing of COVID-19 Preprints in Peer-reviewed Journals, Preprinting Trends, Public Discussion and Quality Issues. Preprint article. bioRxiv 2020.11.23.394577; doi: https://doi.org/10.1101/2020.11.23.394577 .

Dobler CC. Poor quality research and clinical practice during COVID-19. Breathe (Sheff). 2020;16(2):200112. https://doi.org/10.1183/20734735.0112-2020 .

Article   Google Scholar  

Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med. 2010;7(9):e1000326. https://doi.org/10.1371/journal.pmed.1000326 .

Lunny C, Brennan SE, McDonald S, McKenzie JE. Toward a comprehensive evidence map of overview of systematic review methods: paper 1-purpose, eligibility, search and data extraction. Syst Rev. 2017;6(1):231. https://doi.org/10.1186/s13643-017-0617-1 .

Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of Reviews. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.1 (updated September 2020). Cochrane. 2020. Available from www.training.cochrane.org/handbook .

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions version 6.1 (updated September 2020). Cochrane. 2020; Available from www.training.cochrane.org/handbook .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):18. https://doi.org/10.1186/s13643-018-0914-3 .

Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. A decision tool to help researchers make decisions about including systematic reviews in overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):29. https://doi.org/10.1186/s13643-018-0768-8 .

Hunt H, Pollock A, Campbell P, Estcourt L, Brunton G. An introduction to overviews of reviews: planning a relevant research question and objective for an overview. Syst Rev. 2018;7(1):39. https://doi.org/10.1186/s13643-018-0695-8 .

Pollock M, Fernandes RM, Pieper D, Tricco AC, Gates M, Gates A, et al. Preferred reporting items for overviews of reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions. Syst Rev. 2019;8(1):335. https://doi.org/10.1186/s13643-019-1252-9 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med. 2009;3(3):e123–30.

Krnic Martinic M, Pieper D, Glatt A, Puljak L. Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol. 2019;19(1):203. https://doi.org/10.1186/s12874-019-0855-0 .

Puljak L. If there is only one author or only one database was searched, a study should not be called a systematic review. J Clin Epidemiol. 2017;91:4–5. https://doi.org/10.1016/j.jclinepi.2017.08.002 .

Article   PubMed   Google Scholar  

Gates M, Gates A, Guitard S, Pollock M, Hartling L. Guidance for overviews of reviews continues to accumulate, but important challenges remain: a scoping review. Syst Rev. 2020;9(1):254. https://doi.org/10.1186/s13643-020-01509-0 .

Covidence - systematic review software. Available at: https://www.covidence.org/ . Accessed 1 June 2021.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Borges do Nascimento IJ, et al. Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis. J Clin Med. 2020;9(4):941.

Article   PubMed Central   Google Scholar  

Adhikari SP, Meng S, Wu YJ, Mao YP, Ye RX, Wang QZ, et al. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis Poverty. 2020;9(1):29. https://doi.org/10.1186/s40249-020-00646-x .

Cortegiani A, Ingoglia G, Ippolito M, Giarratano A, Einav S. A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. J Crit Care. 2020;57:279–83. https://doi.org/10.1016/j.jcrc.2020.03.005 .

Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol. 2020;109(5):531–8. https://doi.org/10.1007/s00392-020-01626-9 .

Article   CAS   PubMed   Google Scholar  

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577–83. https://doi.org/10.1002/jmv.25757 .

Lippi G, Lavie CJ, Sanchis-Gomar F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): evidence from a meta-analysis. Prog Cardiovasc Dis. 2020;63(3):390–1. https://doi.org/10.1016/j.pcad.2020.03.001 .

Lippi G, Henry BM. Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19). Eur J Intern Med. 2020;75:107–8. https://doi.org/10.1016/j.ejim.2020.03.014 .

Lippi G, Plebani M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chim Acta. 2020;505:190–1. https://doi.org/10.1016/j.cca.2020.03.004 .

Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta. 2020;506:145–8. https://doi.org/10.1016/j.cca.2020.03.022 .

Ludvigsson JF. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 2020;109(6):1088–95. https://doi.org/10.1111/apa.15270 .

Lupia T, Scabini S, Mornese Pinna S, di Perri G, de Rosa FG, Corcione S. 2019 novel coronavirus (2019-nCoV) outbreak: a new challenge. J Glob Antimicrob Resist. 2020;21:22–7. https://doi.org/10.1016/j.jgar.2020.02.021 .

Marasinghe, K.M., A systematic review investigating the effectiveness of face mask use in limiting the spread of COVID-19 among medically not diagnosed individuals: shedding light on current recommendations provided to individuals not medically diagnosed with COVID-19. Research Square. Preprint article. doi : https://doi.org/10.21203/rs.3.rs-16701/v1 . 2020 .

Mullins E, Evans D, Viner RM, O’Brien P, Morris E. Coronavirus in pregnancy and delivery: rapid review. Ultrasound Obstet Gynecol. 2020;55(5):586–92. https://doi.org/10.1002/uog.22014 .

Pang J, Wang MX, Ang IYH, Tan SHX, Lewis RF, Chen JIP, et al. Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel coronavirus (2019-nCoV): a systematic review. J Clin Med. 2020;9(3):623.

Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623. https://doi.org/10.1016/j.tmaid.2020.101623 .

Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. AJR Am J Roentgenol. 2020;215(1):87–93. https://doi.org/10.2214/AJR.20.23034 .

Sun P, Qie S, Liu Z, Ren J, Li K, Xi J. Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis. J Med Virol. 2020;92(6):612–7. https://doi.org/10.1002/jmv.25735 .

Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–5. https://doi.org/10.1016/j.ijid.2020.03.017 .

Bassetti M, Vena A, Giacobbe DR. The novel Chinese coronavirus (2019-nCoV) infections: challenges for fighting the storm. Eur J Clin Investig. 2020;50(3):e13209. https://doi.org/10.1111/eci.13209 .

Article   CAS   Google Scholar  

Hwang CS. Olfactory neuropathy in severe acute respiratory syndrome: report of a case. Acta Neurol Taiwanica. 2006;15(1):26–8.

Google Scholar  

Suzuki M, Saito K, Min WP, Vladau C, Toida K, Itoh H, et al. Identification of viruses in patients with postviral olfactory dysfunction. Laryngoscope. 2007;117(2):272–7. https://doi.org/10.1097/01.mlg.0000249922.37381.1e .

Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many estimates of the COVID-19 case fatality rate. Lancet Infect Dis. 2020;20(7):776–7. https://doi.org/10.1016/S1473-3099(20)30244-9 .

Wolkewitz M, Puljak L. Methodological challenges of analysing COVID-19 data during the pandemic. BMC Med Res Methodol. 2020;20(1):81. https://doi.org/10.1186/s12874-020-00972-6 .

Rombey T, Lochner V, Puljak L, Könsgen N, Mathes T, Pieper D. Epidemiology and reporting characteristics of non-Cochrane updates of systematic reviews: a cross-sectional study. Res Synth Methods. 2020;11(3):471–83. https://doi.org/10.1002/jrsm.1409 .

Runjic E, Rombey T, Pieper D, Puljak L. Half of systematic reviews about pain registered in PROSPERO were not published and the majority had inaccurate status. J Clin Epidemiol. 2019;116:114–21. https://doi.org/10.1016/j.jclinepi.2019.08.010 .

Runjic E, Behmen D, Pieper D, Mathes T, Tricco AC, Moher D, et al. Following Cochrane review protocols to completion 10 years later: a retrospective cohort study and author survey. J Clin Epidemiol. 2019;111:41–8. https://doi.org/10.1016/j.jclinepi.2019.03.006 .

Tricco AC, Antony J, Zarin W, Strifler L, Ghassemi M, Ivory J, et al. A scoping review of rapid review methods. BMC Med. 2015;13(1):224. https://doi.org/10.1186/s12916-015-0465-6 .

COVID-19 Rapid Reviews: Cochrane’s response so far. Available at: https://training.cochrane.org/resource/covid-19-rapid-reviews-cochrane-response-so-far . Accessed 1 June 2021.

Cochrane. Living systematic reviews. Available at: https://community.cochrane.org/review-production/production-resources/living-systematic-reviews . Accessed 1 June 2021.

Millard T, Synnot A, Elliott J, Green S, McDonald S, Turner T. Feasibility and acceptability of living systematic reviews: results from a mixed-methods evaluation. Syst Rev. 2019;8(1):325. https://doi.org/10.1186/s13643-019-1248-5 .

Babic A, Poklepovic Pericic T, Pieper D, Puljak L. How to decide whether a systematic review is stable and not in need of updating: analysis of Cochrane reviews. Res Synth Methods. 2020;11(6):884–90. https://doi.org/10.1002/jrsm.1451 .

Lovato A, Rossettini G, de Filippis C. Sore throat in COVID-19: comment on “clinical characteristics of hospitalized patients with SARS-CoV-2 infection: a single arm meta-analysis”. J Med Virol. 2020;92(7):714–5. https://doi.org/10.1002/jmv.25815 .

Leung C. Comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1431–2. https://doi.org/10.1002/jmv.25912 .

Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al. Response to Char’s comment: comment on Li et al: COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(9):1433. https://doi.org/10.1002/jmv.25924 .

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

This research received no external funding.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

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Thilo Caspar von Groote

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Livia Puljak

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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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• Review Article
• Published: 17 July 2020

A literature review of 2019 novel coronavirus (SARS-CoV2) infection in neonates and children

• Matteo Di Nardo 1 ,
• Grace van Leeuwen 2 ,
• Alessandra Loreti 3 ,
• Maria Antonietta Barbieri 4 ,
• Yit Guner 5 ,
• Franco Locatelli 6 &
• Vito Marco Ranieri 7  

Pediatric Research volume  89 ,  pages 1101–1108 ( 2021 ) Cite this article

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At the time of writing, there are already millions of documented infections worldwide by the novel coronavirus 2019 (2019-nCoV or severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)), with hundreds of thousands of deaths. The great majority of fatal events have been recorded in adults older than 70 years; of them, a large proportion had comorbidities. Since data regarding the epidemiologic and clinical characteristics in neonates and children developing coronavirus disease 2019 (COVID-19) are scarce and originate mainly from one country (China), we reviewed all the current literature from 1 December 2019 to 7 May 2020 to provide useful information about SARS-CoV2 viral biology, epidemiology, diagnosis, clinical features, treatment, prevention, and hospital organization for clinicians dealing with this selected population.

Children usually develop a mild form of COVID-19, rarely requiring high-intensity medical treatment in pediatric intensive care unit.

Vertical transmission is unlikely, but not completely excluded.

Children with confirmed or suspected COVID-19 must be isolated and healthcare workers should wear appropriate protective equipment.

Some clinical features (higher incidence of fever, vomiting and diarrhea, and a longer incubation period) are more common in children than in adults, as well as some radiologic aspects (more patchy shadow opacities on CT scan images than ground-glass opacities).

Supportive and symptomatic treatments (oxygen therapy and antibiotics for preventing/treating bacterial coinfections) are recommended in these patients.

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Introduction.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. 1 Since its first outbreak in Wuhan, in the Hubei province of China in early December 2019, 2 SARS-CoV2 has spread all over the world infecting millions of people and causing hundreds of thousands o deaths [case fatality rate (CFR): 6.25%, John Hopkins Coronavirus Resource Center, accessed 7 May 2020]. 3

Respiratory viral infections, in general, are more frequent and severe in children than in adults. SARS-CoV2, instead, showed a different scenario. Infection rates appear to be similar between children and adults; however, children develop a milder illness with a low CFR (<0.1%). 3 , 4 , 5 , 6 , 7 The reasons for this milder severity in childhood are not yet understood, and the actual epidemiologic and clinical data of infected neonates and children are not sufficient to solve these gaps. Thus, due to the scarcity of data on SARS-CoV2 in children, we aimed at evaluating the current literature available to provide useful information for clinicians dealing with this particular population.

Search strategy

References for this review were identified through searches on PubMED, Ovid MEDLINE, and EMBASE from 1 December 2019 to 7 May 2020, by two highly experienced librarians at Children’s Hospital Bambino Gesù by using relevant terms related to 2019-nCoV, COVID-19, and SARS-CoV2 in neonates and children (Supplementary Material  1 ). Reference lists of the articles identified by this search strategy were also searched. Earlier reports were not excluded, especially if they were highly cited articles. Only articles published in English were included in this review. Three hundred and seventy-four papers were published in PubMed, 117 in Ovid MEDLINE, and 119 in EMBASE. Among them, 73 were deemed relevant to the purposes of this review (PRISMA flowchart Supplementary Material  2 ).

Biological mechanisms of viral infection and lung injury

Coronaviruses are single-strand, positive-sense RNA viruses with spike-like projections on their surface. 8 These viruses can infect both animals and humans. Among human-infecting coronaviruses, four types (HKU1, NL63, 229E, and OC43) are responsible for mild forms of respiratory disease. 9 , 10 SARS-CoV2, SARS-CoV, and the Middle East respiratory syndrome coronavirus (MERS-CoV) are zoonotic viruses and can infect humans, causing severe respiratory infections, only crossing from animals (Fig.  1 ).

Summary of coronavirus diseases (adapted from Zimmermann and Curtis 8 ).

SARS-CoV2 infects the host cells through an envelope spike (S) protein that mediates the binding and membrane fusion through the angiotensin-converting enzyme 2 (ACE-2) receptor (Fig.  2a, b ). The spike protein is functionally divided into an S1 domain, responsible for receptor binding, and an S2 domain, responsible for cell membrane fusion. 11 SARS-CoV2 employs the transmembrane serine protease 2 of the host cell to prime the S protein and bind the ACE-2 receptor. Other transmembrane pore-forming viral proteins (viroporins) can trigger the NLRP3 (NOD-like receptor 3 inflammasome)-inducing pyroptosis in the host cell. 12

a Renin–angiotensin system (RAS): normal physiology. Renin converts angiotensinogen in angiotensin 1 (ANG 1). Angiotensin-converting enzyme (ACE) converts ANG1 in angiotensin 2 (ANG2). Angiotensin-converting enzyme 2 (ACE-2), a homolog of ACE, is a monocarboxypeptidase that converts ANG2 into angiotensin 1–7 (ANG1–7), which, by virtue of its actions on the MasR (mitocondrial assembly receptor), opposes the molecular and cellular effects of ANG2. ANG2 promotes vasoconstriction, inflammation, and oxidative stress via the activation of AT1R (angiotensin 2 receptor 1). b  SARS-CoV2 host cell entry mechanism: Spike protein (S1) binds the ACE-2 receptor once primed by the transmembrane protease serine 2 inhibitor (TMPRSS2). This binding leads to viral entry and replication and induces mechanisms of lung injury. c  Potential therapeutic strategies against SARS-COV2. Spike protein-based vaccine; TMPRSS2 inhibitors to block the priming of the spike protein; surface ACE-2 receptor blocker; soluble form of ACE-2 receptor compete with the binding of SARS-CoV2 to the surface ACE-2 receptor.

ACE-2 receptors are expressed in many tissues; however, the majority are present on the alveolar epithelial type II cells. 13 In addition, gene ontology enrichment analysis showed that the ACE-2-expressing epithelial cells have high levels of multiple viral process-related genes, including regulatory genes for viral processes, life cycle, assembly, and genome replication. 13 All these features strongly support the hypothesis that the ACE-2 receptor mediates SARS-CoV2 replication in the lung. SARS-CoV2, through the binding to the ACE-2 receptor, downregulates the ACE-2 intracellular signaling (mitochondrial assembly receptor), causing inflammation, vasoconstriction, and fibrosis in the lung. 13

Epidemiology and pathogenesis in neonates and children

Published data and anecdotal reports support the notion that the number of children found to be infected by SARS-CoV2 is small and their clinical manifestations of COVID-19 are milder compared to adults. 4 , 5 , 6 , 14 , 15 , 16 , 17 , 18

The incidence of SARS-CoV2 confirmed that pediatric cases are low and variable among countries (China: 2–12.3%, 4 , 5 Italy: 1.2%, 19 Korea: 4.8%, 20 USA: 5% 21 ). Several reasons justify this variable incidence: testing availability, testing policy 22 , 23 (at the beginning of pandemics some countries tested only children with established contact with a person with COVID-19, then only hospitalized children with symptoms), and the fact that the infection in children is mild or without symptoms. 24 , 25 Available data also suggest that all ages (0–18) can be infected, but infants seem to be most vulnerable. 5 , 26

Human-to-human transmission (mainly family clustered) is the major transmission mode. 4 , 5 , 27 Children can be infected by inhalation of large droplets generated during coughing or sneezing or by contact with contaminated surface (fomite). 9 , 10 , 28 , 29 , 30 As the virus can be also released in the stool, the fecal–oral transmission cannot be ruled out. 31 , 32 , 33 , 34 Similar to SARS-CoV and MERS-CoV, nosocomial transmission of SARS-CoV2 is high, 9 , 10 , 35 , 36 although no cases of nosocomial infections have been described in children during hospital recovery.

Despite the absence of clinical features of infection or positive microbiological findings in neonates born from SARS-CoV2-positive mothers, 14 , 18 , 37 , 38 , 39 , 40 , 41 , 42 vertical maternal–fetal transmission cannot be ruled out completely. 43 , 44 Conversely, SARS-CoV2 has not been isolated from cord blood, amniotic fluid, and breast milk to date. However, it is crucial to screen pregnant women, implement strict infection control measures on those who tested positive, and monitor the neonates at risk. 44 , 45

Since the incubation period (median 5–7 days) in children and young adolescent varies from 2 to 14 days, but is generally longer than in adults, 10 , 46 , 47 , 48 dynamic observation is mandatory for suspected children. 49 , 50 The median period from symptom onset to hospital admission for patients who were hospitalized is 2 days (1.00–3.50). Recovery generally happens in 1–2 weeks after onset. 40 , 48 Both symptomatic patients and asymptomatic carriers can transmit SARS-CoV2. 49 , 51 , 52

The basic case reproduction (R0) of SARS-CoV2 is variable (2–3.5 in the early stage of the disease); 9 however, the R0 of SARS-CoV2 is higher than SARS-CoV and H1N1. 10 The CFR is ~6.25% (data from 7 May, John Hopkins Coronavirus Resource Center) 3 and varies among countries, 53 patients’ age, and is influenced by testing availability. 54 CFR of patients below 18 years is below <0.1% (adapted from John Hopkins Coronavirus Resource center at 7 May 2020). 3 , 7

This age specificity is still not completely understood. 24 , 55 It is speculated that children, as compared with adults, may have a higher expression of ACE-2 receptors in the type II lung pneumocytes, protecting them from the severe clinical manifestation of COVID-19 (low cytokine release, low pulmonary vascular permeability, etc.). 55 Other immunologic mechanisms (trained immunity, an early and high polyclonal B cell response to SARS-CoV2 with the production of substantial numbers of plasmablasts, and an high level natural killer cells) could also contribute to explain this age-specific characteristic. 55 , 56 A less intense mechanism of antibody-dependent enhancement, instead, could explain why COVID-19 clinical features are milder in children than in adults. 12

Since the World Health Organization (WHO) recently declared COVID-19 a pandemic on 11 March 2020, every patient presenting with evidence of fever, respiratory symptoms, gastrointestinal symptoms, or fatigue should be considered potentially infected (suspected case) with SARS-CoV-2.

Diagnosis of COVID-19 is made by using real-time polymerase chain reaction (RT-PCR) on samples from nasopharyngeal, oropharyngeal swabs, and lower respiratory tract samples whenever possible. 4 , 5 Negative nasopharyngeal swab is generally re-tested after 24 h due to the low negative predictive value of this testing. 57 SARS-CoV2 can be also detected on stools. 33 , 58 , 59 A “positive” RT-PCR result reflects only the detection of viral RNA and does not necessarily indicate the presence of a viable virus. 52

Confirmed cases are defined by positive molecular tests, while asymptomatic cases are defined by positive molecular tests without symptoms.

In children, more than in adults, COVID-19 poses important diagnostic challenges due to the longer incubation period that includes a prolonged interval (~5–6 days) of viral shedding prior to the onset of symptoms. 51 , 60 Moreover, the duration of asymptomatic shedding is not only variable, but also differs according to the anatomic level (upper versus lower airways) of the infection. 49 , 50

At present, among adult patients in affected areas, the most common cause of viral pneumonia with unclear etiology is SARS-CoV2; 2 conversely, in children several other pathogens (influenza, para-influenza, adenovirus, respiratory syncytial virus, metapneumovirus, or other human coronaviruses) can produce very similar clinical and radiologic findings and should be considered in the differential diagnosis. 6 , 8 , 26 , 61 Atypical microorganisms, such as chlamydia pneumoniae and mycoplasma, must be also excluded. 10

No laboratory investigations and radiological findings are diagnostic of SARS-CoV2. 4 , 5 , 6 , 10 , 47 , 62

Clinical features

Clinical manifestations of COVID-19 in neonates and children reported are generally mild and similar among countries. 4 , 5 , 6 , 14 , 16 , 22 , 23 , 37 , 38 , 46 , 63 , 64 , 65 Most commonly, at hospital admission, children presented with fever and respiratory symptoms with cough, sore throat, pharyngeal erythema, nasal congestion, tachypnea/dyspnea, and tachycardia. 22 , 23 , 65 Often, gastrointestinal symptoms, including abdominal pain, nausea, vomiting, and diarrhea, were the first manifestations. 4 , 5 , 15 , 46 , 64 , 66 Neurological manifestations such as seizures, dystonia, and altered mental status were rare. 66 Neonates, instead, showed tachypnea, cough, grunting, nasal flaring, vomiting, poor feeding, diarrhea, and lethargy. 45 , 61 , 67 , 68 , 69 Hospital admission was higher in Italy and Spain than in China and USA; 4 , 21 , 22 , 65 however, this was mainly due to local policies (testing availability and policy, need of patient isolation) rather than clinical condition. 22 , 65

In the largest retrospective cohort of COVID-19 pediatric patients reported so far [2134 patients including 731 (34.1%) laboratory-confirmed and 1412 (65.9%) suspected cases], Dong et al. 5 defined the severity of COVID-19 in asymptomatic infection, mild, moderate, severe, and critical cases, based on the clinical features, laboratory testing, and X-ray imaging (Table  1 ). In this cohort, 4.4% of infected children were asymptomatic, while the remaining children presented a mild (50.9%) or moderate disease (38.8%), respectively. Only 5.2% had severe disease, while 0.6% had critical disease. The proportion of severe and critical cases was 10.6%, 7.3%, 4.2%, 4.1%, and 3.0% for the age group of <1, 1–5, 6–10, 11–15, and >16 years, respectively.

Lu et al. 4 showed 15.8% of COVID-19 children included in their retrospective cohort (171 SARS-CoV2 confirmed cases) were completely asymptomatic and did not show any radiological findings of pneumonia.

Respiratory coinfections were present in almost half of the cases. 4 , 5 , 26 Comorbidities, as in adult patients, 70 may affect outcome 23 and the likelihood of Pediatric Intensive Care Unit (PICU) admission. 4 , 23

In adults, the incidence of ICU admission was high and variable among countries (5% in China and 9% in Italy); 70 , 71 in children, the incidence was lower (0.21–5.2% among Chinese PICUs, 4 , 5 , 15 0.04% in USA 23 ). Of note, several biases (retrospective nature of these studies, 5 , 61 the proportion of the detected cases, the use of different PICU admission criteria among centers, 5 the use of the same data source with overlapping data—Chinese Centers for Disease Control and Prevention database—and the high number of suspected cases 47 ) could have affected the interpretation of these results.

Most of the laboratory abnormalities in children with COVID-19 are nonspecific. Henry et al. 62 reviewed the data of 66 children from 12 different studies and found that 69.2% of children had normal leukocyte counts and that neutrophilia or neutropenia were rare (<5%). Platelet count was variable among studies (generally higher than the normal range), while C-reactive protein and procalcitonin were increased in 13.6% and 10.6% of the cases, respectively. 62

Children admitted to the PICU 15 showed normal or increased whole blood counts (7/8) and increased C-reactive protein, procalcitonin, and lactate dehydrogenase (6/8). High levels of pro-inflammatory and anti-inflammatory cytokines were also present similarly to the adult patients. 72 , 73

Although lymphocytopenia is very common in adults with severe COVID-19 and associated with worse outcomes, 47 it is less common in children (2–3.5%), likely due to the constitutional high percentage of lymphocytes typical of this age. 62 , 74 In adult patients, high ferritin, high d -dimers, and coagulopathy were associated with poor prognosis, 70 but these laboratory findings were rare in children; high d -dimers levels were found in one of the two patients who died from COVID-19. 4 , 15 However, during April 2020, a surge of anecdotal cases showing a hyper-inflammatory state (pediatric multisystem inflammatory syndrome temporally associated with COVID-19) and features similar to atypical Kawasaki disease or Kawasaki disease shock syndrome were reported in Europe (United Kingdom, Spain, Italy). 75 , 76 Many of these patients had positive SARS-CoV2 antibodies and presented an inflammatory state (elevated concentration of C-reactive protein, procalcitonin, ferritin triglycerides, and d -dimers) with cutaneous rash, peripheral edema, conjunctivitis, myocardial dysfunction (elevated cardiac enzymes), and coronary vessels inflammation.

Radiologic findings of SARS-CoV2 viral pneumonia were also variable among children (Fig.  3 ). 4 At hospital admission, many children presented a chest X-ray showing an interstitial pneumonia, 26 while chest computed tomography (CT) scan showed patchy shadows (unilateral and bilateral) with opacities of high density. The typical adult feature of ground-glass opacity was less frequent at hospital admission (32.7%); 4 instead, it was more common in patients admitted to the PICU for respiratory failure. 4 , 5 , 6 , 26 , 77 , 78 , 79 Bedside lung ultrasonography was also used as a diagnostic tool in the emergency departments in a minority of patients; 80 90% of these received a diagnosis of interstitial lung syndrome without further radiographic imaging. 65

a Chest X ray and b chest computed tomography. Vital signs: respiratory rate 22 breaths/min, SpO 2 : 97% in room air. The patient was supported with high-flow nasal cannula 25 L/min, FiO 2 : 30% in the pediatric ward.

Treatment of COVID-19 in neonates and children mainly relies on supportive care. 4 , 10

Home isolation is the first step to manage children with mild symptoms and no underlying chronic conditions. Hospitalization may be considered if rapid deterioration is anticipated or if the patient is not able to urgently return to hospital when signs and symptoms of complicated disease arise. Moderate cases should be managed in hospital, monitoring vital signs and oxygen saturation. Supportive care for these children includes temperature control with antipyretics, bed rest, hydration, and good nutrition. Routine antibiotics and antifungal drugs must be avoided and used only when coinfections are proven or strongly suspected. 10 , 15

In hypoxic patients, oxygen therapy should be immediately initiated. 81 Several devices [low flow nasal cannula, high-flow nasal cannula (HFNC), and noninvasive ventilation (NIV)] can be used according to the centers’ experience. Caution must be taken, since all noninvasive techniques bear the risk of aerosol contamination; strict personal protection equipment (PPE) must be used when caring for these patients.

Invasive mechanical ventilation is indicated if: SpO 2 /FiO 2  < 221 or if there is no improvement in oxygenation (target SpO 2 92–97% with FiO 2  < 0.4) within 30–60 min of HFNC or if there is no improvement in oxygenation (target SpO 2 92–97% and FiO 2  < 0.6) within 60–90 min of CPAP/NIV. 81 Escalating therapies are recommended in case of refractory hypoxia (surfactant therapy in neonates, inhaled nitric oxide, high frequency oscillatory ventilation, and extracorporeal membrane oxygenation). 81 , 82 , 83

A small portion of children with COVID-19 developed septic shock; 5 , 15 , 84 thus, this condition must be always suspected and managed according to the current pediatric guidelines since specific issues for COVID-19 have not been reported so far. 85 Corticosteroids should not be used in pediatric patients, 86 except when required for other indications, such as asthma exacerbations, refractory shock, or evidence of cytokine storm. 16

Several treatment options (intravenous immunoglobulin, interleukin-1 (IL-1) blockade, IL-6 receptor blockade, azythromycin-chloroquine, plasma exchange, infusion of plasma from convalescent subjects, cytokine adsorption filters) have been used in critically ill adult patients; however, data on their efficacy and safety have not been reported yet, thus caution should be used also in children. 87

Antiviral drugs should be used with caution after weighing advantages and disadvantages. For those with mild symptoms, low dosage of interferon-α nebulization has been used 16 in combination with oral ribavirin. Lopinavir/litonavir 15 and remdesivir 88 , 89 have been used in more severe cases; however, their efficacy and safety in children remain to be determined. 90 Remdesivir should be preferred in children because of its positive effects in a recent adult trial; 88 , 89 however, when not available, or when patients are not good candidate to remdesivir, hydroxychloroquine could be considered. 88 The combination of three or more antiviral drugs is generally not recommended. 90

Potential therapeutic strategies for SARS-COV2 are the spike protein-based vaccine, the inhibitors of transmembrane protease serine 2 activity, and the delivery of excessive soluble form of ACE-2 or antibody against the surface of ACE-2 receptors (Fig.  2c ). 13

Prevention and healthcare organization

COVID-19 has no approved treatment in neonates and children and a large-scale vaccine is still under development; thus, prevention is crucial. 10 , 91

SARS-CoV-2 has unique characteristics that makes its prevention complex. SARS-CoV-2 can cause an asymptomatic infection, can be transmitted during the incubation period and after clinical recovery, 13 has a very high affinity to ACE-2 receptors, which are expressed on many mucosal surfaces, resulting in high transmissibility, and can be spread also by fomite. 10

The high transmissibility and low CFR, combined with the discouraging projections of the spread of the virus among adults, 70 fostered many governments, at the beginning of March 2020, to adopt stringent containment and self-isolation measures to reduce the spread of the virus. An intense public health response was started by many countries after the pandemic declaration and involved many strategies: lockdown of the cities and mass quarantine, social distancing mandates, schools closure, cancellation of public gatherings, reduction of domestic and international flights, development of environmental measures and personal protection procedures, and strict contacts tracings by the medical and public health professionals. These measures aim to delay major surges of patients and to lower the demand for hospital extra beds, while protecting the most vulnerable subjects from infection, especially the elderly and those with comorbidities. 92

Data showed that pediatric cases requiring high-intensity medical assistance are uncommon; 5 , 15 however, isolation of all suspected and confirmed patients remains mandatory to avoid the spread of SARS-CoV2 among caregivers and healthcare workers. Therefore, many pediatric hospitals have developed local guidelines and logistic plans (simulations and training courses, reduction of elective surgeries and visits to outpatient clinics, etc.) to identify in advance potential surge capacity in the form of dedicated environment with extra beds for isolation, quarantine, and dedicated staff. As stocks of PPE might run low during a period of pandemic, strict hospital policies should also be adopted according to the WHO guidelines. 93 Furthermore, considering the high number of adult ICU admissions and the difficulties associated to create extra beds in a short period of time, 70 pediatric intensivists and nurses should be ready and prepared to offer help by managing adult patients in PICU 94 or to help in adult ICUs.

Differently from adults, home isolation is not easily performed in children, because they often require the presence of the parents, limiting the use of protective distances (>1.5 m). In those cases, all people sharing a common environment with a SARS-CoV2-positive child should consider the use of gloves and face masks, if available. Hand hygiene practices are extremely important to prevent the spread of the COVID-19 virus at home and in public environments. The WHO recommends washing hands, especially after coughing or sneezing (including sneeze/cough into elbow or tissue), before eating and after using the toilet or sharing common spaces. 95 Hand washing also interrupts transmission of other viruses and bacteria causing common colds, flu, and pneumonia, thus reducing the general burden of disease. Relatives at risk (e.g., people over the age of 65 years, pregnant women, people who are immunocompromised or who have chronic heart, lung, or kidney conditions) 96 should be isolated in protected environment, avoiding exposures to infected children. Because infants cannot wear masks, parents must wear masks, wash hands before close contacts, and sterilize the toys and tablet regularly. 97

All suspected children requiring hospital assistance must be isolated in single rooms (whenever possible, or in dedicated environments, maintaining adequate distances between beds) until the results of the test are available; confirmed patients must be placed in dedicated area for quarantine. A dedicated algorithm must be adopted for the use of the operating theaters in suspected or confirmed COVID-19 cases, according to the urgency of the operation, anticipated viral burden at the surgical site, and the risk that a procedure could spread the virus by aereosol. 98 , 99 Negative pressure rooms are of help, but not mandatory to manage these patients. 10 All rooms and transition environments must be decontaminated after the patient discharge (Fig.  3 ).

Since a high number of health care workers has been infected by SARS-CoV2, all suspected patients, until proven negative, must be assisted by health care providers using PPE 93 and all aerosol generating procedures (intubation, bronchoscopy, tube/tracheostomy suctioning, etc.) must be also performed using airborne transmission precautions. 93

Enhanced traffic control bundling strategies must be adopted by all emergency departments, 100 including a triage zone, transition zones conduction to a quarantine ward or to an isolation ward (Fig.  4 ). A dedicated pathway for children non-SARS-CoV2 suspected (e.g., trauma, poisoning, etc.) must also be created in parallel to avoid contact. Telemedicine should be implemented to help reduce hospital and clinic visits, 101 , 102 by triaging low-acuity patients while delivering high-quality care. 103

Enhanced traffic control system used in Children’s Hospital Bambino Gesù, Rome, Italy.

The scarcity of pediatric cases and the current literature on the topic, as well as the absence of high-quality evidence-based guidelines, has led pediatricians to share experiences and personal communication via online meetings and open access medical education channels. The use of webinars and communication about newly released papers on social media channels such as Twitter, Telegram and WhatsApp, greatly improved the dissemination of knowledge among health care providers.

At the time of this review (7 May 2020), SARS-CoV2 has infected millions of people in the world and caused hundreds of thousands confirmed deaths, but data regarding the epidemiologic and clinical characteristics in neonates and children are still scarce. The purpose of this review was to evaluate the current literature that includes neonates and children to date, providing useful information for clinicians dealing with this selected population. The earliest epidemiologic data show that SARS-CoV2 has a dominant family-cluster transmission and that children present a mild form of COVID-19 (CFR: <0.1%), rarely requiring high-intensity medical treatment in PICU. Vertical transmission is unlikely, but not completely excluded. Diagnosis is performed primarily via molecular nucleic acid amplification testing. Patients with confirmed or suspected COVID-19 should be isolated and healthcare workers should wear appropriate protective equipment. Some clinical features (higher incidence of fever, vomiting and diarrhea, and a longer incubation period) are more common in children than in adults, as well as some radiologic aspects, including the presence of patchy shadow opacities on CT scan images. Treatment options are extrapolated from adult data. Thus, supportive and symptomatic treatments (oxygen therapy and antibiotics for bacterial coinfections) are recommended in these patients. More studies on neonates and children are needed to address these gaps and to provide more robust recommendations to manage COVID-19.

Lai, C. C., Shih, T. P., Ko, W. C., Tang, H. J. & Hsueh, P. R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges. Int. J. Antimicrob. Agents 55 , 105924 (2020).

CAS   PubMed   PubMed Central   Google Scholar  

Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382 , 727–733 (2020).

Center for Systems Science and Engineering (CSSE) at JHU. COVID-19 Dashboard. https://coronavirus.jhu.edu/map .

Lu, X. et al. SARS-CoV-2 infection in children. N. Engl. J. Med. 382 , 1663–1665 (2020).

PubMed   Google Scholar  

Dong, Y. et al. Epidemiology of COVID-19 among children in China. Pediatrics 145 , e20200702 (2020).

Liu, W. et al. Detection of Covid-19 in children in early January 2020 in Wuhan. China. N. Engl. J. Med. 382 , 1370–1371 (2020).

Jeng, M. J. COVID-19 in children: current status. J. Chin. Med. Assoc. 83 , 527–533 (2020).

CAS   PubMed   Google Scholar  

Zimmerman, P. & Curtis, N. Coronavirus infections in children including COVID-19: an overview of the epidemiology, clinical features, diagnosis, treatment and prevention options in children. Pediatr. Infect. Dis. J. 39 , 355–368 (2020).

Google Scholar  

Wang, Y., Wang, Y., Chen, Y. & Qin, Q. Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. J. Med. Virol. 92 , 568–576 (2020).

Singhal, T. A Review of Coronavirus Disease-2019 (COVID-19). Indian J Pediatr 87 , 281–286 (2020).

PubMed   PubMed Central   Google Scholar  

Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395 , 565–574 (2020).

Fu, Y., Cheng, Y. & Wu, Y. Understanding SARS-CoV-2-mediated inflammatory responses: from mechanisms to potential therapeutic tools. Virol. Si. 35 , 266–271 (2020).

CAS   Google Scholar  

Zhang, H., Penninger, J. M., Li, Y., Zhong, N. & Slutsky, A. S. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med . 46 , 586–590 (2020).

Cai, J. et al. A case series of children with 2019 novel coronavirus infection: clinical and epidemiological features. Clin. Infect. Dis . Feb;ciaa198 (2020).

Sun, D. et al. Clinical features of severe pediatric patients with coronavirus disease 2019 in Wuhan: a single center’s observational study. World J. Pediatr. 16 , 251–259 (2020).

Shen, K. et al. Diagnosis, treatment, and prevention of 2019 novel coronavirus infection in children: experts’ consensus statement. World J. Pediatr. 16 , 223–231 (2020).

Stower, H. Clinical and epidemiological characteristics of children with COVID-19. Nat. Med . 26 , 465 (2020).

Yang, P. et al. Clinical characteristics and risk assessment of newborns born to mothers with COVID-19. J. Clin. Virol . 127 , 104356 (2020).

Livingston E., Bucher K. Coronavirus Disease 2019 (COVID-19) in Italy. Jama (2020).

Korean Society of Infectious Diseases. Report on the Epidemiological Features of Coronavirus Disease 2019 (COVID-19) Outbreak in the Republic of Korea from January 19 to March 2, 2020. J. Korean Med. Sci. 35 , e112 (2020).

CDC COVID-19 Response Team. Coronavirus Disease 2019 in Children — United States, February 12–April 2, 2020. MMWR Morb Mortal Wkly Rep 2020;69:422–426. https://doi.org/10.15585/mmwr.mm6914e44e4 .

Tagarro, A. et al. Screening and severity of coronavirus disease 2019 (COVID-19) in children in Madrid, Spain. JAMA Pediatr. 8 , e201346 (2020).

Pathak, E. B., Salemi, J. L., Sobers, N., Menard, J. & Hambleton, I. R. COVID-19 in Children in the United States: intensive care admissions, estimated total infected, and projected numbers of severe pediatric cases in 2020. J. Public Health Manag. Pract. 26 , 325–333 (2020).

Brodin, P. Why is COVID-19 so mild in children? Acta Paediatr. 109 , 1082–1083 (2020).

Lee, P. I., Hu, Y. L., Chen, P. Y., Huang, Y. C. & Hsueh, P. R. Are children less susceptible to COVID-19? J. Microbiol. Immunol. Infect. 53 , 371–372 (2020).

Xia, W. et al. Clinical and CT features in pediatric patients with COVID-19 infection: different points from adults. Pediatr. Pulmonol. 55 , 1169–1174 (2020).

Su, L. et al. The different clinical characteristics of corona virus disease cases between children and their families in China - the character of children with COVID-19. Emerg Microbes Infect 9 , 707–713 (2020).

Cheng, Z. J. & Shan, J. 2019 Novel coronavirus: where we are and what we know. Infection 48 , 155–163 (2020).

van Doremalen, N. et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N. Engl. J. Med. 382 , 1564–1567 (2020).

Yung, C. F. et al. Environment and personal protective equipment tests for SARS-CoV-2 in the isolation room of an infant with infection. Ann. Intern. Med . M20-0942 (2020).

Ma, X. et al. Do children need a longer time to shed SARS-CoV-2 in stool than adults? J. Microbiol. Immunol. Infect. 53 , 373–376 (2020).

Yeo, C., Kaushal, S. & Yeo, D. Enteric involvement of coronaviruses: is faecal-oral transmission of SARS-CoV-2 possible? Lancet Gastroenterol Hepatol 5 , 335–337 (2020).

Tian, Y., Rong, L., Nian, W. & He, Y. Review article: gastrointestinal features in COVID-19 and the possibility of faecal transmission. Aliment. Pharmacol. Ther. 51 , 843–851 (2020).

Dona, D., Minotti, C., Costenaro, P., Da Dalt, L. & Giaquinto, C. Fecal–oral transmission of Sars-Cov-2 in children: is it time to change our approach? Pediatr. Infect. Dis. J. 39 , e133–e134 (2020).

Bartoszko, J. J., Farooqi, M. A. M., Alhazzani, W. & Loeb, M. Medical masks vs N95 respirators for preventing COVID-19 in health care workers. A systematic review and meta-analysis of randomized trials. Influenza Other Respir. Viruses 14 , 365–373 (2020).

Ferioli, M. et al. Protecting healthcare workers from SARS-CoV-2 infection: practical indications. Eur. Respir. Rev. 29 , 200068 (2020).

Zhu, H. et al. Clinical analysis of 10 neonates born to mothers with 2019-nCoV pneumonia. Transl Pediatr . 9 , 51–60 (2020).

Chen, H. et al. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. Lancet 395 , 809–815 (2020).

Schwartz, D. A. An analysis of 38 pregnant women with COVID-19, their newborn infants, and maternal–fetal transmission of SARS-CoV-2: maternal coronavirus infections and pregnancy outcomes. Arch. Pathol. Lab. Med. https://doi.org/10.5858/arpa.2020-0901-SA (2020).

Hong, H., Wang, Y., Chung, H. T. & Chen, C. J. Clinical characteristics of novel coronavirus disease 2019 (COVID-19) in newborns, infants and children. Pediatr. Neonatol. 61 , 131–132 (2020).

Li, Y. et al. Lack of vertical transmission of severe acute respiratory syndrome coronavirus 2. China. Emerg. Infect. Dis. 26 , 1335–1336 (2020).

Zeng, H. et al. Antibodies in infants born to mothers with COVID-19 pneumonia. JAMA 16 , 223–231 (2020).

Zeng, L. et al. Neonatal early-onset infection with SARS-CoV-2 in 33 neonates born to mothers with COVID-19 in Wuhan, China. JAMA Pediatr. 174 , 722–725 (2020).

Buonsenso, D. et al. Neonatal late onset infection with severe acute respiratory syndrome coronavirus 2. Am. J. Perinatol. 37 , 869–872 (2020).

Wang, L. et al. Chinese expert consensus on the perinatal and neonatal management for the prevention and control of the 2019 novel coronavirus infection (First edition. Ann. Transl. Med . 8 , 47 (2020).

Wang, X. F. et al. [Retracted: Clinical and epidemiological characteristics of 34 children with 2019 novel coronavirus infection in Shenzhen]. Zhonghua Er Ke Za Zhi 58 , E008 (2020).

Ludvigsson, J. F. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 109 , 1088–1095 (2020).

Cao, Q., Chen, Y. C., Chen, C. L. & Chiu, C. H. SARS-CoV-2 infection in children: Transmission dynamics and clinical characteristics. J. Formos. Med. Assoc . 119 , 670–673 (2020).

Kam, K. Q. et al. A well infant with coronavirus disease 2019 (COVID-19) with high viral load. Clin. Infect. Dis . ciaa201 (2020).

Lai, C. C. et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): facts and myths. J. Microbiol. Immunol. Infect. 53 , 404–412 (2020).

He, X. et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat. Med. 26 , 672–675 (2020).

Wolfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 581 , 465–469 (2020).

Streeck, H., Hartmann, G., Exner, M., & Schmid, M. Vorläufiges Ergebnis und Schlussfolgerungen der COVID-19 Case-Cluster-Study (Gemeinde Gangelt). https://www.land.nrw/sites/default/files/asset/document/zwischenergebnis_covid19_case_study_gangelt_0.pdf .

Bendavid, E. et al. COVID-19 Antibody Seroprevalence in Santa Clara County, California. medRxiv 2020.04.14.20062463. https://doi.org/10.1101/2020.04.14.20062463 .

Cristiani, L. et al. Will children reveal their secret? The coronavirus dilemma. Eur. Respir. J. 55 , 2001617 (2020).

Carsetti, R. Q. C. et al. The immune system of children: the key to understanding SARS-CoV-2 susceptibility? Lancet Child Adolesc. Health 4 , 414–416 (2020).

Chu, D. K. W. et al. Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia. Clin. Chem . 66 , 549–555 (2020).

Zhang, T. et al. Detectable SARS-CoV-2 viral RNA in feces of three children during recovery period of COVID-19 pneumonia. J. Med. Virol. 92 , 909–914 (2020).

Xing, Y. H. et al. Prolonged viral shedding in feces of pediatric patients with coronavirus disease 2019. J. Microbiol. Immunol. Infect. 53 , 473–480 (2020).

Lu, Y. et al. Symptomatic infection is associated with prolonged duration of viral shedding in mild coronavirus disease 2019: a retrospective study of 110 children in Wuhan. Pediatr. Infect. Dis. J. 39 , e95–e99 (2020).

Lu, Q. & Shi, Y. Coronavirus disease (COVID-19) and neonate: What neonatologist need to know. J. Med. Virol. 92 , 564–567 (2020).

Henry, B. M., Lippi, G. & Plebani, M. Laboratory abnormalities in children with novel coronavirus disease 2019. Clin. Chem. Lab. Med. 58 , 1135–1138 (2020).

Yang, P., Liu, P., Li, D. & Zhao, D. Corona virus disease 2019, a growing threat to children? J. Infect. 80 , 671–693 (2020).

Zheng, F. et al. Clinical characteristics of children with coronavirus disease 2019 in Hubei. China. Curr. Med. Sci. 40 (Apr), 275–280 (2020).

Parri, N., Lenge, M. & Buonsenso, D. Children with Covid-19 in pediatric emergency departments in Italy. N. Engl. J. Med. 383 , 187–190 (2020).

Dugue, R. et al. Neurologic manifestations in an infant with COVID-19. Neurology 94 , 1100–1102 (2020).

De Luca, D. Managing neonates with respiratory failure due to SARS-CoV-2. Lancet Child Adolesc. Health 4 , e8 (2020).

Li, F., Feng, Z. C. & Shi Y. Proposal for prevention and control of the 2019 novel coronavirus disease in newborn infants. Arch. Dis. Child Fetal Neonatal Ed . fetalneonatal-2020-318996 (2020).

Wang, J., Qi, H., Bao, L., Li, F. & Shi, Y. A contingency plan for the management of the 2019 novel coronavirus outbreak in neonatal intensive care units. Lancet Child Adolesc. Health 4 , 258–259 (2020).

Grasselli, G. et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA 323 , 1574–1581 (2020).

Guan, W. J. et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382 , 1708–1720 (2020).

Mehta, P. et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395 , 1033–1034 (2020).

Licciardi, F. et al. COVID-19 and what pediatric rheumatologists should know: a review from a highly affected country. Pediatr Rheumatol Online J 18 , 35 (2020).

Li, H., Chen, K., Liu, M., Xu, H. & Xu, Q. The profile of peripheral blood lymphocyte subsets and serum cytokines in children with 2019 novel coronavirus pneumonia. J. Infect. 81 , 115–120 (2020).

Riphagen, S. G. X., Gonzalez-Matinez, C., Wilkinson, N. & Theocharis, P. Hyperinflammatory shock in children during COVID-19 pandemic. Lancet 395 , 1607–1608 (2020).

Jones, V. G. et al. COVID-19 and Kawasaki disease: novel virus and novel case. Hosp. Pediatr. 10 , 537–540 (2020).

Li, W., Cui, H., Li, K., Fang, Y. & Li, S. Chest computed tomography in children with COVID-19 respiratory infection. Pediatr. Radiol . 50 , 796–799 (2020).

Feng, K. et al. [Analysis of CT features of 15 children with 2019 novel coronavirus infection]. Zhonghua Er Ke Za Zhi 58 , 275–278 (2020).

Mungmunpuntipantip, R. & Wiwanitkit, V. Chest computed tomography in children with COVID-19. Pediatr. Radiol. 50 , 1018 (2020).

Denina, M. et al. Lung ultrasound in children with COVID-19. Pediatrics 146 , e20201157 (2020).

Di Nardo, M. et al. A literature review of 2019 novel coronavirus (SARS-CoV2) infection in neonates and children. Pediatr Res (In Press, 2020). https://doi.org/10.1038/s41390-020-1065-5

ECMO in COVID-19. https://www.elso.org/COVID19.aspx .

Kneyber, M. C. J. et al. Recommendations for mechanical ventilation of critically ill children from the Paediatric Mechanical Ventilation Consensus Conference (PEMVECC). Intensive Care Med . 43 , 1764–1780 (2017).

Cui, Y. et al. A 55-day-old female infant infected with COVID 19: presenting with pneumonia, liver injury, and heart damage. J. Infect. Dis. 221 , 1775–1781 (2020).

Weiss, S. L. et al. Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-Associated Organ Dysfunction in Children. Pediatr. Crit. Care Med . 21 , e52–e106 (2020).

Russell, C. D., Millar, J. E. & Baillie, J. K. Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury. Lancet 395 , 473–475 (2020).

Chen, Z. M. et al. Diagnosis and treatment recommendations for pediatric respiratory infection caused by the 2019 novel coronavirus. World J. Pediatr. 16 , 240–246 (2020).

Chiotos, K. et al. Multicenter initial guidance on use of antivirals for children with COVID-19/SARS-CoV-2. J. Pediatr. Infect. Dis. Soc . piaa045 (2020).

Grein, J. et al. Compassionate use of remdesivir for patients with severe Covid-19. N. Engl. J. Med. 382 , 2327–2336 (2020).

Wang, Y. & Zhu, L. Q. Pharmaceutical care recommendations for antiviral treatments in children with coronavirus disease 2019. World J. Pediatr. 16 , 271–274 (2020).

Kelvin, A. A. & Halperin, S. COVID-19 in children: the link in the transmission chain. Lancet Infect. Dis. 20 , 633–634 (2020).

Bedford, J. et al. COVID-19: towards controlling of a pandemic. Lancet 395 , 1015–1018 (2020).

World Health Organization. Coronavirus disease (COVID-19) outbreak: rights,roles and responsibilities of health workers, including key considerations for occupational safety and health. (2020). https://www.who.int/publications/i/item/coronavirus-disease-(covid-19)-outbreak-rights-roles-and-responsibilities-of-health-workers-including-key-considerations-for-occupational-safety-and-health .

PICS. PICS and ICS Joint Position Statement (12 Mar 2020). https://picsociety.uk/news/pics-and-ics-joint-position-statement-12-mar-2020/ .

World Health Organization. Interim recommendations on obligatory hand hygiene against transmission of COVID-19. (2020) https://www.who.int/publications/m/item/interim-recommendations-on-obligatory-hand-hygiene-against-transmission-of-covid-19 . Accessed 7 th May 2020

Kotecha, R. S. Challenges posed by COVID-19 to children with cancer. Lancet Oncol. 21 , e235 (2020).

Wei, M. et al. Novel coronavirus infection in hospitalized infants under 1 year of age in China. JAMA 323 , 1313–1314 (2020).

Forrester, J. D., Nassar, A. K., Maggio, P. M. & Hawn, M. T. Precautions for operating room team members during the COVID-19 Pandemic. J. Am. Coll. Surg. 230 , 1098–1101 (2020).

Zhou, Y., Xu, H., Li, L. & Ren, X. Management for patients with pediatric surgical disease during the COVID-19 epidemic. Pediatr. Surg. Int 36 , 751–752 (2020).

Yen, M. Y. et al. Interrupting COVID-19 transmission by implementing enhanced traffic control bundling: Implications for global prevention and control efforts. J. Microbiol. Immunol. Infect. 53 , 377–380 (2020).

Woo Baidal, J. A. et al. Zooming towards a telehealth solution for vulnerable children with obesity during COVID-19. Obesity (Silver Spring) 28 , 1184–1186 (2020).

Verstraete, S. G., Sola, A. M. & Ali, S. A. Telemedicine for Pediatric Inflammatory bowel disease in the Era of COVID-19. J. Pediatr. Gastroenterol. Nutr. 70 , e140 (2020).

Rockwell, K. L. & Gilroy, A. S. Incorporating telemedicine as part of COVID-19 outbreak response systems. Am. J. Manag. Care 26 , 147–148 (2020).

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Each author made a substantial contribution to this review and met the Pediatric Research authorship requirements. M.D.N., G.V.L., and A.L. contributed to the review design, data acquisition, and screening. M.D.N., M.A.B., and Y.G. contributed to the interpretation of the data and article drafting. M.D.N., F.L., and V.M.R. contributed to the article drafting and revisions. All authors have approved the final manuscript.

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Di Nardo, M., van Leeuwen, G., Loreti, A. et al. A literature review of 2019 novel coronavirus (SARS-CoV2) infection in neonates and children. Pediatr Res 89 , 1101–1108 (2021). https://doi.org/10.1038/s41390-020-1065-5

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Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis

Linked editorial.

Public health measures for covid-19

• Related content
• Peer review
• Shivangi Shah , honours student 1 ,
• Holly Wild , lecturer and honours student 1 3 ,
• Danijela Gasevic , senior lecturer in epidemiology and chronic disease prevention 1 4 ,
• Ashika Maharaj , lecturer quality and safety and cancer epidemiology 1 ,
• Zanfina Ademi , associate professor of medical outcomes and health economics 1 2 ,
• Xue Li , assistant professor 4 6 ,
• Wei Xu , research student 4 ,
• Ines Mesa-Eguiagaray , statistical geneticist 4 ,
• Jasmin Rostron , research student 4 ,
• Evropi Theodoratou , professor of cancer epidemiology and global health 4 5 ,
• Xiaomeng Zhang , research student 4 ,
• Ashmika Motee , research student 4 ,
• Danny Liew , professor of medical outcomes and health economics 1 2 ,
• Dragan Ilic , professor of medical education and public health 1
• 1 School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004 VIC, Australia
• 2 Monash Outcomes Research and health Economics (MORE) Unit, Monash University, VIC, Australia
• 3 Torrens University, VIC, Australia
• 4 Centre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK
• 5 Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
• 6 School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
• Correspondence to: S Talic stella.talic{at}monash.edu
• Accepted 21 October 2021

Objective To review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.

Design Systematic review and meta-analysis.

Data sources Medline, Embase, CINAHL, Biosis, Joanna Briggs, Global Health, and World Health Organization COVID-19 database (preprints).

Eligibility criteria for study selection Observational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.

Main outcome measures The main outcome measure was incidence of covid-19. Secondary outcomes included SARS-CoV-2 transmission and covid-19 mortality.

Data synthesis DerSimonian Laird random effects meta-analysis was performed to investigate the effect of mask wearing, handwashing, and physical distancing measures on incidence of covid-19. Pooled effect estimates with corresponding 95% confidence intervals were computed, and heterogeneity among studies was assessed using Cochran’s Q test and the I 2 metrics, with two tailed P values.

Results 72 studies met the inclusion criteria, of which 35 evaluated individual public health measures and 37 assessed multiple public health measures as a “package of interventions.” Eight of 35 studies were included in the meta-analysis, which indicated a reduction in incidence of covid-19 associated with handwashing (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I 2 =12%), mask wearing (0.47, 0.29 to 0.75, I 2 =84%), and physical distancing (0.75, 0.59 to 0.95, I 2 =87%). Owing to heterogeneity of the studies, meta-analysis was not possible for the outcomes of quarantine and isolation, universal lockdowns, and closures of borders, schools, and workplaces. The effects of these interventions were synthesised descriptively.

Conclusions This systematic review and meta-analysis suggests that several personal protective and social measures, including handwashing, mask wearing, and physical distancing are associated with reductions in the incidence covid-19. Public health efforts to implement public health measures should consider community health and sociocultural needs, and future research is needed to better understand the effectiveness of public health measures in the context of covid-19 vaccination.

Systematic review registration PROSPERO CRD42020178692.

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Introduction

The impact of SARS-CoV-2 on global public health and economies has been profound. 1 As of 14 October 2021, there were 239 007 759 million cases of confirmed covid-19 and 4 871 841 million deaths with covid-19 worldwide. 2

A variety of containment and mitigation strategies have been adopted to adequately respond to covid-19, with the intention of deferring major surges of patients in hospitals and protecting the most vulnerable people from infection, including elderly people and those with comorbidities. 3 Strategies to achieve these goals are diverse, commonly based on national risk assessments that include estimation of numbers of patients requiring hospital admission and availability of hospital beds and ventilation support.

Globally, vaccination programmes have proved to be safe and effective and save lives. 4 5 Yet most vaccines do not confer 100% protection, and it is not known how vaccines will prevent future transmission of SARS-CoV-2, 6 given emerging variants. 7 8 9 The proportion of the population that must be vaccinated against covid-19 to reach herd immunity depends greatly on current and future variants. 10 This vaccination threshold varies according to the country and population’s response, types of vaccines, groups prioritised for vaccination, and viral mutations, among other factors. 6 Until herd immunity to covid-19 is reached, regardless of the already proven high vaccination rates, 11 public health preventive strategies are likely to remain as first choice measures in disease prevention, 12 particularly in places with a low uptake of covid-19 vaccination. Measures such as lockdown (local and national variant), physical distancing, mandatory use of face masks, and hand hygiene have been implemented as primary preventive strategies to curb the covid-19 pandemic. 13

Public health (or non-pharmaceutical) interventions have been shown to be beneficial in fighting respiratory infections transmitted through contact, droplets, and aerosols. 14 15 Given that SARS-CoV-2 is highly transmissible, it is a challenge to determine which measures might be more effective and sustainable for further prevention.

Substantial benefits in reducing mortality were observed in countries with universal lockdowns in place, such as Australia, New Zealand, Singapore, and China. Universal lockdowns are not, however, sustainable, and more tailored interventions need to be considered; the ones that maintain social lives and keep economies functional while protecting high risk individuals. 16 17 Substantial variation exists in how different countries and governments have applied public health measures, 18 and it has proved a challenge for assessing the effectiveness of individual public health measures, particularly in policy decision making. 19

Previous systematic reviews on the effectiveness of public health measures to treat covid-19 lacked the inclusion of analytical studies, 20 a comprehensive approach to data synthesis (focusing only on one measure), 21 a rigorous assessment of effectiveness of public health measures, 22 an assessment of the certainty of the evidence, 23 and robust methods for comparative analysis. 24 To tackle these gaps, we performed a systematic review of the evidence on the effectiveness of both individual and multiple public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality. When feasible we also did a critical appraisal of the evidence and meta-analysis.

This systematic review and meta-analysis were conducted in accordance with PRISMA 25 (supplementary material 1, table 1) and with PROSPERO (supplementary material 1, table 2).

Eligibility criteria

Articles that met the population, intervention, comparison, outcome, and study design criteria were eligible for inclusion in this systematic review (supplementary material 1, table 3). Specifically, preventive public health measures that were tested independently were included in the main analysis. Multiple measures, which generally contain a “package of interventions”, were included as supplementary material owing to the inability to report on the individual effectiveness of measures and comparisons on which package led to enhanced outcomes. The public health measures were identified from published World Health Organization sources that reported on the effectiveness of such measures on a range of communicable diseases, mostly respiratory infections, such as influenza.

Given that the scientific community is concerned about the ability of the numerous mathematical models, which are based on assumptions, to predict the course of virus transmission or effectiveness of interventions, 26 this review focused only on empirical studies. We excluded case reports and case studies, modelling and simulation studies, studies that provided a graphical summary of measures without clear statistical assessments or outputs, ecological studies that provided a descriptive summary of the measures without assessing linearity or having comparators, non‐empirical studies (eg, commentaries, editorials, government reports), other reviews, articles involving only individuals exposed to other pathogens that can cause respiratory infections, such as severe acute respiratory syndrome or Middle East respiratory syndrome, and articles in a language other than English.

Information sources

We carried out electronic searches of Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature, Ebsco), Global Health, Biosis, Joanna Briggs, and the WHO COVID-19 database (for preprints). A clinical epidemiologist (ST) developed the initial search strategy, which was validated by two senior medical librarians (LR and MD) (supplementary material 1, table 4). The updated search strategy was last performed on 7 June 2021. All citations identified from the database searches were uploaded to Covidence, an online software designed for managing systematic reviews, 27 for study selection.

Study selection

Authors ST, DG, SS, AM, ET, JR, XL, WX, IME, and XZ independently screened the titles and abstracts and excluded studies that did not match the inclusion criteria. Discrepancies were resolved in discussion with the main author (ST). The same authors retrieved full text articles and determined whether to include or exclude studies on the basis of predetermined selection criteria. Using a pilot tested data extraction form, authors ST, SS, AM, JR, XL, WX, AM, IME, and XZ independently extracted data on study design, intervention, effect measures, outcomes, results, and limitations. ST, SS, AM, and HW verified the extracted data. Table 5 in supplementary material 1 provides the specific criteria used to assess study designs. Given the heterogeneity and diversity in how studies defined public health measures, we took a common approach to summarise evidence of these interventions (supplementary material 1, table 6).

Risk of bias within individual studies

SS, JR, XL, WX, IME, and XZ independently assessed risk of bias for each study, which was cross checked by ST and HW. For non-interventional observational studies, a ROBINS-I (risk of bias in non-randomised studies of interventions) risk of bias tool was used. 28 For interventional studies, a revised tool for assessing risk of bias in randomised trials (RoB 2) tool was used. 29 Reviewers rated each domain for overall risk of bias as low, moderate, high, or serious/critical.

Data synthesis

The DerSimonian and Laird method was used for random effects meta-analysis, in which the standard error of the study specific estimates was adjusted to incorporate a measure of the extent of variation, or heterogeneity, among the effects observed for public health measures across different studies. It was assumed that the differences between studies are a result of different, yet related, intervention effects being estimated. If fewer than five studies were included in meta-analysis, we applied a recommended modified Hartung-Knapp-Sidik-Jonkman method. 30

Statistical analysis

Because of the differences in the effect metrics reported by the included studies, we could only perform quantitative data synthesis for three interventions: handwashing, face mask wearing, and physical distancing. Odds ratios or relative risks with corresponding 95% confidence intervals were reported for the associations between the public health measures and incidence of covid-19. When necessary, we transformed effect metrics derived from different studies to allow pooled analysis. We used the Dersimonian Laird random effects model to estimate pooled effect estimates along with corresponding 95% confidence intervals for each measure. Heterogeneity among individual studies was assessed using the Cochran Q test and the I 2 test. 31 All statistical analyses were conducted in R (version 4.0.3) and all P values were two tailed, with P=0.05 considered to be significant. For the remaining studies, when meta-analysis was not feasible, we reported the results in a narrative synthesis.

Public and patient involvement

No patients or members of the public were directly involved in this study as no primary data were collected. A member of the public was, however, asked to read the manuscript after submission.

A total of 36 729 studies were initially screened, of which 36 079 were considered irrelevent. After exclusions, 650 studies were eligible for full text review and 72 met the inclusion criteria. Of these studies, 35 assessed individual interventions and were included in the final synthesis of results ( fig 1 ) and 37 assessed multiple interventions as a package and are included in supplementary material 3, tables 2 and 3. The included studies comprised 34 observational studies and one interventional study, eight of which were included in the meta-analysis.

Flow of articles through the review. WHO=World Health Organization

Risk of bias

According to the ROBINS-I tool, 28 the risk of bias was rated as low in three studies, 32 33 34 moderate in 24 studies, 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 and high to serious in seven studies. 59 60 61 62 63 64 65 One important source of serious or critical risk of bias in most of the included studies was major confounding, which was difficult to control for because of the novel nature of the pandemic (ie, natural settings in which multiple interventions might have been enforced at once, different levels of enforcement across regions, and uncaptured individual level interventions such as increased personal hygiene). Variations in testing capacity and coverage, changes to diagnostic criteria, and access to accurate and reliable outcome data on covid-19 incidence and covid-19 mortality, was a source of measurement bias for numerous studies ( fig 2 ). These limitations were particularly prominent early in the pandemic, and in low income environments. 47 52 62 63 65 The randomised controlled trial 66 was rated as moderate risk of bias according to the ROB-2 tool. Missing data, losses to follow-up, lack of blinding, and low adherence to intervention all contributed to the reported moderate risk. Tables 1 and 2 in supplementary material 2 summarise the risk of bias assessment for each study assessing individual measures.

Summary of risk of bias across studies assessing individual measures using risk of bias in non-randomised studies of interventions (ROBINS-I) tool

Study characteristics

Studies assessing individual measures.

Thirty five studies provided estimates on the effectiveness of an individual public health measures. The studies were conducted in Asia (n=11), the United States (n=9), Europe (n=7), the Middle East (n=3), Africa (n=3), South America (n=1), and Australia (n=1). Thirty four of the studies were observational and one was a randomised controlled trial. The study designs of the observational studies comprised natural experiments (n=11), quasi-experiments (n=3), a prospective cohort (n=1), retrospective cohorts (n=8), case-control (n=2), and cross sectional (n=9). Twenty six studies assessed social measures, 32 34 35 37 38 39 40 41 42 44 46 47 48 52 53 55 56 57 58 59 60 61 63 64 65 67 12 studies assessed personal protective measures, 36 43 45 49 50 57 58 60 63 66 68 three studies assessed travel related measures, 54 58 62 and one study assessed environmental measures 57 (some interventions overlapped across studies). The most commonly measured outcome was incidence of covid-19 (n=18), followed by SARS-CoV-2 transmission, measured as reproductive number, growth number, or epidemic doubling time (n=13), and covid-19 mortality (n=8). Table 1 in supplementary material 3 provides detailed information on each study.

Effects of interventions

Personal protective measures.

Handwashing and covid-19 incidence —Three studies with a total of 292 people infected with SARS-CoV-2 and 10 345 participants were included in the analysis of the effect of handwashing on incidence of covid-19. 36 60 63 Overall pooled analysis suggested an estimated 53% non-statistically significant reduction in covid-19 incidence (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I 2 =12%) ( fig 3 ). A sensitivity analysis without adjustment showed a significant reduction in covid-19 incidence (0.49, 0.33 to 0.72, I 2 =12%) ( fig 4 ). Risk of bias across the three studies ranged from moderate 36 60 to serious or critical 63 ( fig 2 ).

Meta-analysis of evidence on association between handwashing and incidence of covid-19 using modified Hartung-Knapp-Sidik-Jonkman adjusted random effect model

Meta-analysis of evidence on association between handwashing and incidence of covid-19 using unadjusted random effect model

Mask wearing and covid-19 incidence —Six studies with a total of 2627 people with covid-19 and 389 228 participants were included in the analysis examining the effect of mask wearing on incidence of covid-19 ( table 1 ). 36 43 57 60 63 66 Overall pooled analysis showed a 53% reduction in covid-19 incidence (0.47, 0.29 to 0.75), although heterogeneity between studies was substantial (I 2 =84%) ( fig 5 ). Risk of bias across the six studies ranged from moderate 36 57 60 66 to serious or critical 43 63 ( fig 2 ).

Study characteristics and main results from studies that assessed individual personal protective and environmental measures

• View inline

Meta-analysis of evidence on association between mask wearing and incidence of covid-19 using unadjusted random effect model

Mask wearing and transmission of SARS-CoV-2, covid-19 incidence, and covid-19 mortality —The results of additional studies that assessed mask wearing (not included in the meta-analysis because of substantial differences in the assessed outcomes) indicate a reduction in covid-19 incidence, SARS-CoV-2 transmission, and covid-19 mortality. Specifically, a natural experiment across 200 countries showed 45.7% fewer covid-19 related mortality in countries where mask wearing was mandatory ( table 1 ). 49 Another natural experiment study in the US reported a 29% reduction in SARS-CoV-2 transmission (measured as the time varying reproductive number Rt) (risk ratio 0.71, 95% confidence interval 0.58 to 0.75) in states where mask wearing was mandatory. 58

A comparative study in the Hong Kong Special Administrative Region reported a statistically significant lower cumulative incidence of covid-19 associated with mask wearing than in selected countries where mask wearing was not mandatory ( table 1 ). 68 Similarly, another natural experiment involving 15 US states reported a 2% statistically significant daily decrease in covid-19 transmission (measured as case growth rate) at ≥21 days after mask wearing became mandatory, 50 whereas a cross sectional study reported that a 10% increase in self-reported mask wearing was associated with greater odds for control of SARS-CoV-2 transmission (adjusted odds ratio 3.53, 95% confidence interval 2.03 to 6.43). 45 The five studies were rated at moderate risk of bias ( fig 2 ).

Environmental measures

Disinfection in household and covid-19 incidence.

Only one study, from China, reported the association between disinfection of surfaces and risk of secondary transmission of SARS-CoV-2 within households ( table 1 ). 57 The study assessed disinfection retrospectively by asking participants about their “daily use of chlorine or ethanol-based disinfectant in households,” and observed that use of disinfectant was 77% effective at reducing SARS-CoV-2 transmission (odds ratio 0.23, 95% confidence interval 0.07 to 0.84). The study did not collect data on the concentration of the disinfectant used by participants and was rated at moderate risk of bias ( fig 2 ).

Social measures

Physical distancing and covid-19 incidence.

Five studies with a total of 2727 people with SARS-CoV-2 and 108 933 participants were included in the analysis that examined the effect of physical distancing on the incidence of covid-19. 37 53 57 60 63 Overall pooled analysis indicated a 25% reduction in incidence of covid-19 (relative risk 0.75, 95% confidence interval 0.59 to 0.95, I 2 =87%) ( fig 6 ). Heterogeneity among studies was substantial, and risk of bias ranged from moderate 37 53 57 60 to serious or critical 63 ( fig 2 ).

Meta-analysis of evidence on association between physical distancing and incidence of covid-19 using unadjusted random effect model

Physical distancing and transmission of SARS-CoV-2 and covid-19 mortality

Studies that assessed physical distancing but were not included in the meta-analysis because of substantial differences in outcomes assessed, generally reported a positive effect of physical distancing ( table 2 ). A natural experiment from the US reported a 12% decrease in SARS-CoV-2 transmission (relative risk 0.88, 95% confidence interval 0.86 to 0.89), 40 and a quasi-experimental study from Iran reported a reduction in covid-19 related mortality (β −0.07, 95% confidence interval −0.05 to −0.10; P<0.001). 47 Another comparative study in Kenya also reported a reduction in transmission of SARS-CoV-2 after physical distancing was implemented, reporting 62% reduction in overall physical contacts (reproductive number pre-intervention was 2.64 and post-intervention was 0.60 (interquartile range 0.50 to 0.68)). 61 These three studies were rated at moderate risk of bias 40 61 to serious or critical risk of bias 47 ( fig 2 ).

Study characteristics and main results from studies assessing individual social measures

Stay at home or isolation and transmission of SARS-CoV-2

All the studies that assessed stay at home or isolation measures reported reductions in transmission of SARS-CoV-2 ( table 2 ). A retrospective cohort study from the US reported a significant reduction in the odds of having a positive reproductive number (R0) result (odds ratio 0.07, 95% confidence interval 0.01 to 0.37), 41 and a natural experiment reported a 51% reduction in time varying reproductive number (Rt) (risk ratio 0.49, 95% confidence interval 0.43 to 0.54). 58

A study from the UK reported a 74% reduction in the average daily number of contacts observed for each participant and estimated a decrease in reproductive number: the reproductive number pre-intervention was 3.6 and post-intervention was 0.60 (95% confidence interval 0.37 to 0.89). 65 Similarly, an Iranian study projected the reproductive number using serial interval distribution and the number of incidence cases and found a significant decrease: the reproductive number pre-intervention was 2.70 and post-intervention was 1.13 (95% confidence interval 1.03 to 1.25). 55 Three of the studies were rated at moderate to serious or critical risk of bias, 55 58 65 and one study was rated at low risk of bias 41 ( fig 2 ).

Quarantine and incidence and transmission of SARS-CoV-2

Quarantine was assessed in two studies ( table 2 ). 34 59 A prospective cohort study from Saudi Arabia reported a 4.9% decrease in the incidence of covid-19 at eight weeks after the implementation of quarantine. 34 This study was rated at low risk of bias ( fig 2 ). A retrospective cohort study from India reported a 14 times higher risk of SARS-CoV-2 transmission associated with no quarantine compared with strict quarantine (odds ratio 14.44, 95% confidence interval 2.42 to 86.17). 59 This study was rated at moderate risk of bias ( fig 2 ).

School closures and covid-19 incidence and covid-19 mortality

Two studies assessed the effectiveness of school closures on transmission of SARS-CoV-2, incidence of covid-19, or covid-19 mortality ( table 2 ). 44 48 A US population based longitudinal study reported on the effectiveness of state-wide closure of primary and secondary schools and observed a 62% decrease (95% confidence interval −49% to −71%) in incidence of covid-19 and a 58% decrease (−46% to−68%) in covid-19 mortality. 48 Conversely, a natural experiment from Japan reported no effect of school closures on incidence of covid-19 (α coefficient 0.08, 95% confidence interval −0.36 to 0.65). 44 Both studies were rated at moderate risk of bias ( fig 2 ).

School closures and transmission of SARS-CoV-2

Two natural experiments from the US reported a reduction in transmission (ie, reproductive number); with one study reporting a reduction of 13% (relative risk 0.87, 95% confidence interval 0.86 to 0.89) 40 and another reporting a 10% (0.90, 0.86 to 0.93) reduction ( table 2 ). 58 A Swedish study reported an association between school closures and a small increase in confirmed SARS-CoV-2 infections in parents (odds ratio 1.17, 95% confidence interval 1.03 to 1.32), but observed that teachers in lower secondary schools were twice as likely to become infected than teachers in upper secondary schools (2.01, 1.52 to 2.67). 32 All three studies were rated at moderate risk of bias ( fig 2 ).

Business closures and transmission of SARS-CoV-2

Two natural experiment studies assessed business closures across 50 US states and reported reductions in transmission of SARS-CoV-2 ( table 2 ). 40 58 One of the studies observed a significant reduction in transmission of 12% (relative risk 0.88, 95% confidence interval 0.86 to 0.89) 40 and the other reported a significant 16% (risk ratio 0.84, 0.79 to 0.90) reduction. 58 Both studies were rated at moderate risk of bias ( fig 2 ).

Lockdown and incidence of covid-19

A natural experiment involving 202 countries suggested that countries that implemented universal lockdown had fewer new cases of covid-19 than countries that did not (β coefficient −235.8 (standard error −11.04), P<0.01) ( table 2 ). 52 An Indian quasi-experimental study reported a 10.8% reduction in incidence of covid-19 post-lockdown, 56 whereas a South African retrospective cohort study observed a 14.1% reduction in risk after implementation of universal lockdown ( table 2 ). 46 These studies were rated at high risk of bias 52 and moderate risk of bias 46 56 ( fig 2 ).

Lockdown and covid-19 mortality

The three studies that assessed universal lockdown and covid-19 mortality generally reported a decrease in mortality ( table 2 ). 35 38 42 A natural experiment study involving 45 US states reported a decrease in covid-19 related mortality of 2.0% (95% confidence interval −3.0% to 0.9%) daily after lockdown had been made mandatory. 35 A Brazilian quasi-experimental study reported a 27.4% average difference in covid-19 related mortality rates in the first 25 days of lockdown. 42 In addition, a natural experiment study reported about 30% and 60% reductions in covid-19 related mortality post-lockdown in Italy and Spain over four weeks post-intervention, respectively. 38 All three studies were rated at moderate risk of bias ( fig 2 ).

Lockdown and transmission of SARS-CoV-2

Four studies assessed universal lockdown and transmission of SARS-CoV-2 during the first few months of the pandemic ( table 2 ). The decrease in reproductive number (R0) ranged from 1.27 in Italy (pre-intervention 2.03, post-intervention 0.76) 39 to 2.09 in India (pre-intervention 3.36, post-intervention 1.27), 64 and 3.97 in China (pre-intervention 4.95, post-intervention 0.98). 33 A natural experiment from the US reported that lockdown was associated with an 11% reduction in transmission of SARS-CoV-2 (relative risk 0.89, 95% confidence interval 0.88 to 0.91). 40 All the studies were rated at low risk of bias 33 39 to moderate risk 40 64 ( fig 2 ).

Travel related measures

Restricted travel and border closures.

Border closure was assessed in one natural experiment study involving nine African countries ( table 3 ). 62 Overall, the countries recorded an increase in the incidence of covid-19 after border closure. These studies concluded that the implementation of border closures within African countries had minimal effect on the incidence of covid-19. The study had important limitations and was rated at serious or critical risk of bias. In the US, a natural experiment study reported that restrictions on travel between states contributed about 11% to a reduction in SARS-CoV-2 transmission ( table 3 ). 36 The study was rated at moderate risk of bias ( fig 2 ).

Study characteristics and main results from studies that assessed individual travel measures

Entry and exit screening (virus or symptom screening)

One retrospective cohort study assessed screening of symptoms, which involved testing 65 000 people for fever ( table 3 ). 54 The study found that screening for fever lacked sensitivity (ranging from 18% to 24%) in detecting people with SARS-CoV-2 infection. This translated to 86% of the population with SARS-CoV-2 remaining undetected when screening for fever. The study was rated at moderate risk of bias ( fig 2 ).

Multiple public health measures

Overall, 37 studies provided estimates on the effectiveness of multiple public health measures, assessed as a collective group. Studies were mostly conducted in Asia (n=15), the US (n=11), Europe (n=6), Africa (n=4), and South America (n=1). All the studies were observational. The most commonly measured outcome was transmission of disease (ie, measured as reproductive number, growth number, or epidemic doubling time) (n=23), followed by covid-19 incidence (n=19) and covid-19 mortality (n=8). This review attempted to assess the overall effectiveness of the public health intervention packages by reporting the percentage difference in outcome before and after implementation of measures or between regions or countries studied. Eleven of the 37 included studies noted a difference of between 26% and 50% in transmission of SARS-CoV-2 and incidence of covid-19, 70 71 72 73 74 75 76 77 78 79 80 nine noted a difference of between 51% and 75% in SARS-CoV-2 transmission, covid-19 incidence, and covid-19 mortality, 81 82 83 84 85 86 87 88 89 and 14 noted a difference of more than 75% in transmission of SARS-CoV-2, covid-19 incidence and covid-19 mortality. 79 80 89 90 91 92 93 94 95 96 97 98 99 100 For the remaining studies, the overall effectiveness was not assessed owing to a lack of comparators (see supplementary material 3, table 3). Two studies that assessed universal lockdown and physical distancing reported a decrease of between 0% and 25% in SARS-CoV-2 transmission and covid-19 incidence. 79 101 Studies that included school and workplace closures, 91 95 96 isolation or stay at home measures, 80 94 or a combination of both 79 89 93 97 98 99 reported decreases of more than 75% in SARS-CoV-2 transmission. Supplementary material 3, table 2 provides detailed information on each study.

Worldwide, government and public health organisations are mitigating the spread of SARS-CoV-2 by implementing various public health measures. This systematic review identified a statistically significant reduction in the incidence of covid-19 through the implementation of mask wearing and physical distancing. Handwashing interventions also indicated a substantial reduction in covid-19 incidence, albeit not statistically significant in the adjusted model. As the random effects model tends to underestimate confidence intervals when a meta-analysis includes a small number of individual studies (<5), the adjusted model for handwashing showed a statistically non-significant association in reducing the incidence of covid-19 compared with the unadjusted model.

Overall effectiveness of these interventions was affected by clinical heterogeneity and methodological limitations, such as confounding and measurement bias. It was not possible to evaluate the impact of type of face maks (eg, surgical, fabric, N95 respirators) and compliance and frequency of wearing masks owing to a lack of data. Similarly, it was not feasible to assess the differences in effect that different recommendations for physical distancing (ie, 1.5 m, 2m, or 3 m) have as preventive strategies.

The effectiveness of measures such as universal lockdowns and closures of businesses and schools for the containment of covid-19 have largely been effective, but depended on early implementation when incidence rates of covid-19 were still low. 42 52 58 Only Japan reported no decrease in covid-19 incidence after school closures, 44 and other studies found that different public health measures were sometimes implemented simultaneously or soon after one another, thus the results should be interpreted with caution. 32 46 56

Isolation or stay at home was an effective measure in reducing the transmission of SARS-CoV-2, but the included studies used results for mobility to assess stay at home or isolation and therefore could have been limited by potential flaws in publicly available phone data, 41 58 102 and variations in the enforcement of public health measures in different states or regions were not assessed. 55 58 102 Quarantine was found to be as effective in reducing the incidence of covid-19 and transmission of SARS-CoV-2, yet variation in testing and case detection in low income environments was substantial. 59 96 98 Another study reported that quarantine was effective in reducing the transmission of SARS-CoV-2 in a cohort with a low prevalence of the virus, yet it is unknown if the same effect would be observed with higher prevalence. 34

It was not possible to draw conclusions about the effectiveness of restricted travel and full border closures because the number of empirical studies was insufficient. Single studies identified that border closure in Africa had a minimal effect in reducing SARS-CoV-2 transmission, but the study was assessed as being at high risk of bias. 62 Screening for fever was also identified to be ineffective, with only 24% of positive cases being captured by screening. 54

Comparison with other studies

Previous literature reviews have identified mask wearing as an effective measure for the containment of SARS-CoV-2 103 ; the caveat being that more high level evidence is required to provide unequivocal support for the effectiveness of the universal use of face masks. 104 105 Additional empirical evidence from a recent randomised controlled trial (originally published as a preprint) indicates that mask wearing achieved a 9.3% reduction in seroprevalence of symptomatic SARS-CoV-2 infection and an 11.9% reduction in the prevalence of covid-19-like symptoms. 106 Another systematic review showed stronger effectiveness with the use of N95, or similar, respirators than disposable surgical masks, 107 and a study evaluating the protection offered by 18 different types of fabric masks found substantial heterogeneity in protection, with the most effective mask being multilayered and tight fitting. 108 However, transmission of SARS-CoV-2 largely arises in hospital settings in which full personal protective measures are in place, which suggests that when viral load is at its highest, even the best performing face masks might not provide adequate protection. 51 Additionally, most studies that assessed mask wearing were prone to important confounding bias, which might have altered the conclusions drawn from this review (ie, effect estimates might have been underestimated or overestimated or can be related to other measures that were in place at the time the studies were conducted). Thus, the extent of such limitations on the conclusions drawn remain unknown.

A 2020 rapid review concluded that quarantine is largely effective in reducing the incidence of covid-19 and covid-19 mortality. However, uncertainty over the magnitude of such an effect still remains, 109 with enhanced management of quality quarantine facilities for improved effective control of the epidemics urgently needed. 110 In addition, findings on the application of school and workplace closures are still inconclusive. Policy makers should be aware of the ambiguous evidence when considering school closures, as other potentially less disruptive physical distancing interventions might be more appropriate. 21 Numerous findings from studies on the efficacy of school closures showed that the risk of transmission within the educational environment often strongly depends on the incidence of covid-19 in the community, and that school closures are most successfully associated with control of SARS-CoV-2 transmission when other mitigation strategies are in place in the community. 111 112 113 114 115 116 117 School closures have been reported to be disruptive to students globally and are likely to impair children’s social, psychological, and educational development 118 119 and to result in loss of income and productivity in adults who cannot work because of childcare responsibilities. 120

Speculation remains as how best to implement physical distancing measures. 121 Studies that assess physical distancing measures might interchangeably study physical distancing with lockdown 35 52 56 64 and other measures and thus direct associations are difficult to assess.

Empirical evidence from restricted travel and full border closures is also limited, as it is almost impossible to study these strategies as single measures. Current evidence from a recent narrative literature review suggested that control of movement, along with mandated quarantine, travel restrictions, and restricting nationals from entering areas of high infection, are effective measures, but only with good compliance. 122 A narrative literature review of travel bans, partial lockdowns, and quarantine also suggested effectiveness of these measures, 123 and another rapid review further supported travel restrictions and cross border restrictions to stop the spread of SARS-CoV-2. 124 It was impossible to make such observations in the current review because of limited evidence. A German review, however, suggested that entry, exit, and symptom screening measures to prevent transmission of SARS-CoV-2 are not effective at detecting a meaningful proportion of cases, 125 and another review using real world data from multiple countries found that border closures had minimal impact on the control of covid-19. 126

Although universal lockdowns have shown a protective effect in lowering the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality, these measures are also disruptive to the psychosocial and mental health of children and adolescents, 127 global economies, 128 and societies. 129 Partial lockdowns could be an alternative, as the associated effectiveness can be high, 125 especially when implemented early in an outbreak, 85 and such measures would be less disruptive to the general population.

It is important to also consider numerous sociopolitical and socioeconomic factors that have been shown to increase SARS-CoV-2 infection 130 131 and covid-19 mortality. 132 Immigration status, 82 economic status, 81 101 and poverty and rurality 98 can influence individual and community compliance with public health measures. Poverty can impact the ability of communities to physically distance, 133 especially in crowded living environments, 134 135 as well as reduce access to personal protective measures. 134 135 A recent study highlights that “a one size fits all” approach to public health measures might not be effective at reducing the spread of SARS-CoV-2 in vulnerable communities 136 and could exacerbate social and economic inequalities. 135 137 As such, a more nuanced and community specific approach might be required. Even though screening is highly recommended by WHO 138 because a proportion of patients with covid-19 can be asymptomatic, 138 screening for symptoms might miss a larger proportion of the population with covid-19. Hence, temperature screening technologies might need to be reconsidered and evaluated for cost effectiveness, given such measures are largely depended on symptomatic fever cases.

Strengths and limitations of this review

The main strength of this systematic review was the use of a comprehensive search strategy to identify and select studies for review and thereby minimise selection bias. A clinical epidemiologist developed the search strategy, which was validated by two senior medical librarians. This review followed a comprehensive appraisal process that is recommended by the Cochrane Collaboration 31 to assess the effectiveness of public health measures, with specifically validated tools used to independently and individually assess the risk of bias in each study by study design.

This review has some limitations. Firstly, high quality evidence on SARS CoV-2 and the effectiveness of public health measures is still limited, with most studies having different underlying target variables. Secondly, information provided in this review is based on current evidence, so will be modified as additional data become available, especially from more prospective and randomised studies. Also, we excluded studies that did not provide certainty over the effect measure, which might have introduced selection bias and limited the interpretation of effectiveness. Thirdly, numerous studies measured interventions only once and others multiple times over short time frames (days v month, or no timeframe). Additionally, the meta-analytical portion of this study was limited by significant heterogeneity observed across studies, which could neither be explored nor explained by subgroup analyses or meta-regression. Finally, we quantitatively assessed only publications that reported individual measures; studies that assessed multiple measures simultaneously were narratively analysed with a broader level of effectiveness (see supplementary material 3, table 3). Also, we excluded studies in languages other than English.

Methodological limitations of studies included in the review

Several studies failed to define and assess for potential confounders, which made it difficult for our review to draw a one directional or causal conclusion. This problem was mainly because we were unable to study only one intervention, given that many countries implemented several public health measures simultaneously; thus it is a challenge to disentangle the impact of individual interventions (ie, physical distancing when other interventions could be contributing to the effect). Additionally, studies measured different primary outcomes and in varied ways, which limited the ability to statistically analyse other measures and compare effectiveness.

Further pragmatic randomised controlled trials and natural experiment studies are needed to better inform the evidence and guide the future implementation of public health measures. Given that most measures depend on a population’s adherence and compliance, it is important to understand and consider how these might be affected by factors. A lack of data in the assessed studies meant it was not possible to understand or determine the level of compliance and adherence to any of the measures.

Conclusions and policy implications

Current evidence from quantitative analyses indicates a benefit associated with handwashing, mask wearing, and physical distancing in reducing the incidence of covid-19. The narrative results of this review indicate an effectiveness of both individual or packages of public health measures on the transmission of SARS-CoV-2 and incidence of covid-19. Some of the public health measures seem to be more stringent than others and have a greater impact on economies and the health of populations. When implementing public health measures, it is important to consider specific health and sociocultural needs of the communities and to weigh the potential negative effects of the public health measures against the positive effects for general populations. Further research is needed to assess the effectiveness of public health measures after adequate vaccination coverage has been achieved. It is likely that further control of the covid-19 pandemic depends not only on high vaccination coverage and its effectiveness but also on ongoing adherence to effective and sustainable public health measures.

What is already known on this topic

Public health measures have been identified as a preventive strategy for influenza pandemics

The effectiveness of such interventions in reducing the transmission of SARS-CoV-2 is unknown

What this study adds

The findings of this review suggest that personal and social measures, including handwashing, mask wearing, and physical distancing are effective at reducing the incidence of covid-19

More stringent measures, such as lockdowns and closures of borders, schools, and workplaces need to be carefully assessed by weighing the potential negative effects of these measures on general populations

Further research is needed to assess the effectiveness of public health measures after adequate vaccination coverage

Ethics statements

Ethical approval.

Not required.

Data availability statement

No additional data available.

Acknowledgments

We thank medical subject librarians Lorena Romero (LR) and Marshall Dozier (MD) for their expert advice and assistance with the study search strategy.

Contributors: ST, DG, DI, DL, and ZA conceived and designed the study. ST, DG, SS, AM, HW, WX, JR, ET, AM, XL, XZ, and IME collected and screened the data. ST, DG, and DI acquired, analysed, or interpreted the data. ST, HW, and SS drafted the manuscript. All authors critically revised the manuscript for important intellectual content.. XL and ST did the statistical analysis. NA obtained funding. LR and MD provided administrative, technical, or material support. ST and DI supervised the study. ST and DI had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. ST is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: No funding was available for this research. ET is supported by a Cancer Research UK Career Development Fellowship (grant No C31250/A22804). XZ is supported by The Darwin Trust of Edinburgh.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: ET is supported by a Cancer Research UK Career Development Fellowship and XZ is supported by The Darwin Trust of Edinburgh; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

The lead author (ST) affirms that the manuscript is an honest, accurate, and transparent account of the study reported; no important aspects of the study have been omitted. Dissemination to participants and related patient and public communities: It is anticipated to disseminate the results of this research to wider community via press release and social media platforms.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• ↵ World Health Organization. WHO Coronavirus (COVID-19) Dashboard. 2021. https://covid19.who.int/
• Parodi SM ,
• Bernal JL ,
• Andrews N ,
• Chodick G ,
• Patalon T ,
• Anderson RM ,
• Vegvari C ,
• Truscott J ,
• Khateeb J ,
• McArthur AG ,
• Banerjee A ,
• Sanyaolu A ,
• Marinkovic A ,
• ↵ World Health Organization. Coronavirus disease (COVID-19): Herd immunity, lockdowns and COVID-19. 2020. www.who.int/news-room/q-a-detail/herd-immunity-lockdowns-and-covid-19
• Stehlik P ,
• Glasziou PP
• ↵ World Health Organization. COVID-19 strategy update. 2020. www.who.int/docs/default-source/coronaviruse/covid-strategy-update-14april2020.pdf?sfvrsn=29da3ba0_19
• Hollingsworth TD ,
• Klinkenberg D ,
• Heesterbeek H ,
• Anderson RM
• Aledort JE ,
• Wasserman J ,
• Bozzette SA
• ↵ World Health Organization. Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza 2019. 2019. https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1 .
• Yang Chan EY ,
• Shahzada TS ,
• Hellewell J ,
• Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group
• Mendez-Brito A ,
• El Bcheraoui C ,
• Pozo-Martin F
• Russell SJ ,
• Craig KJT ,
• Maatoug J ,
• Liberati A ,
• Tetzlaff J ,
• Altman DG ,
• PRISMA Group
• Holmdahl I ,
• ↵ Covidence Systematic Review Software. Veritas Health Innovation, Melbourne Australia. www.covidence.org
• Sterne JA ,
• Hernán MA ,
• Reeves BC ,
• Sterne JAC ,
• Savović J ,
• ↵ Higgins JPTTJ, Chandler J, Cumpston M, Li T, Page MJ. Welch VA, ed. Cochrane Handbook for Systematic Reviews of Interventions. : Chichester, UK: Wiley; 2019. 2nd edn. https://training.cochrane.org/handbook .
• Vlachos J ,
• Hertegård E ,
• B Svaleryd H
• Al-Tawfiq JA ,
• Al-Khadra H ,
• Siedner MJ ,
• Harling G ,
• Reynolds Z ,
• Cheong HH ,
• Van den Berg P ,
• Schechter-Perkins EM ,
• Guzzetta G ,
• Riccardo F ,
• Marziano V ,
• COVID-19 Working Group,2
• McAuley FM ,
• Figueiredo Filho D ,
• Fernandes A
• Krishnamachari B ,
• Zastrow D ,
• Santella AJ
• Miyakoshi C
• Motloba P ,
• Motaung KSC ,
• Alimohamadi Y ,
• Holakouie-Naieni K ,
• Sepandi M ,
• Richardson T ,
• Leffler CT ,
• Lykins JD ,
• McKeown CA ,
• Grzybowski A
• Luckhoff C ,
• Mitchell RD ,
• O’Reilly GM ,
• Khosravi A ,
• Rohani-Rasaf M ,
• Mehravaran S ,
• Thayer WM ,
• Sankhla P ,
• Valamparampil MJ ,
• Varghese B ,
• van Zandvoort K ,
• CMMID COVID-19 Working Group
• Ilesanmi OS
• Doung-Ngern P ,
• Suphanchaimat R ,
• Panjangampatthana A ,
• Salvatore M ,
• Jarvis CI ,
• Van Zandvoort K ,
• CMMID COVID-19 working group
• Bundgaard H ,
• Bundgaard JS ,
• Raaschou-Pedersen DET ,
• Cheng VC-C ,
• Chuang VW-M ,
• Malheiro R ,
• Figueiredo AL ,
• Magalhães JP ,
• Dasgupta S ,
• Kassem AM ,
• Sunshine G ,
• Bendavid E ,
• Bhattacharya J ,
• Ioannidis JPA
• Rothenbühler M ,
• Fisher BT ,
• Khosrawipour V ,
• Kocbach P ,
• Athotra A ,
• Vaisakh TP ,
• NCDC COVID Incident Management Team
• Courtemanche C ,
• Garuccio J ,
• Pinkston J ,
• Al Wahaibi A ,
• Al Manji A ,
• Al Maani A ,
• Timelli L ,
• Undurraga EA ,
• Laborde CC ,
• Bhattacharyya R ,
• Castillo RC ,
• Staguhn ED ,
• Weston-Farber E
• Cruz-Cano R ,
• Venkataramani A ,
• Gilbert RF ,
• Tchole AIM ,
• Cheeloo EcoHealth Consortium (CLEC)
• McCreesh N ,
• Dlamini V ,
• Edwards A ,
• Haapanen M ,
• McGrail DJ ,
• McAndrews KM ,
• Clipman SJ ,
• Wesolowski AP ,
• Gibson DG ,
• Brainard J ,
• Camargo MC ,
• Martinez-Silveira MS ,
• ↵ Abaluck J, Kwong LH, Styczynskyi A, et al. The Impact of Community Masking on COVID-19: A Cluster-Randomized Trial in Bangladesh. 2021. www.poverty-action.org/sites/default/files/publications/Mask_RCT____Symptomatic_Seropositivity_083121.pdf
• MacDougall CC ,
• Johnstone J ,
• Schwartz B ,
• O’Kelly E ,
• Nussbaumer-Streit B ,
• Dobrescu AI ,
• ↵ Nafees M, Khan F. Pakistan’s response to COVID-19 pandemic and efficacy of quarantine and partial lockdown: A review. Electronic Journal of General Medicine 2020;17:em240.
• Macartney K ,
• Pillsbury AJ ,
• NSW COVID-19 Schools Study Team
• Link-Gelles R ,
• DellaGrotta AL ,
• Fontanet A ,
• Tondeur L ,
• ↵ National Centre for Immunisation Research and Surveliance. COVID-19 in schools and early childhood education and care services – the Term 3 experience in NSW. 2020, NSW government. www.ncirsorgau/sites/default/files/2020-10/COVID-19%20Transmission%20in%20educational%20settings%20in%20NSW%20Term%203%20report_0pdf
• ↵ National Centre for Immunisation Research and Surveliance. COVID-19 in schools and early childhood education and care services – the Term 1 experience in NSW. 2020, NSW government. www.ncirsorgau/sites/default/files/2020-08/COVID-19%20Transmission%20in%20educational%20settings%20in%20NSW%20Term%201%20report_0pdf
• ↵ National Centre for Immunisation Research and Surveliance. COVID-19 in schools and early childhood education and care services – the Term 2 experience in NSW. 2020, NSW government. www.ncirsorgau/sites/default/files/2020-08/COVID-19%20Transmission%20in%20educational%20settings%20in%20NSW%20Term%202%20report_0pdf
• Chatterjee S ,
• Fenichel EP
• Bhattacharya S ,
• Chakraborty A
• Stephen S ,
• Movsisyan A ,
• Stratil JM ,
• Geyrhofer L ,
• Patiño-Lugo DF ,
• Velásquez Salazar P ,
• Parveen S ,
• ↵ Felsenthal M. COVID-19 to plunge global economy into worst recession since World War II 2020. www.worldbank.org/en/news/press-release/2020/06/08/covid-19-to-plunge-global-economy-into-worst-recession-since-world-war-ii .
• Brodeur A ,
• Powdthavee N
• Niedzwiedz CL ,
• O’Donnell CA ,
• Chadeau-Hyam M ,
• Bodinier B ,
• Elliott J ,
• Williamson E ,
• Walker AJ ,
• Bhaskaran K ,
• Garnier R ,
• Benetka JR ,
• Kraemer J ,
• Brasher C ,
• Chikumba E ,
• McDougall R ,
• Mellin-Olsen J ,
• Corburn J ,
• Cooney RE ,
• ↵ World Health Organization. Transmission of COVID-19 by asymptomatic cases. 2020. www.emro.who.int/health-topics/corona-virus/transmission-of-covid-19-by-asymptomatic-cases.html

• Research article
• Open access
• Published: 09 January 2021

A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being

• Johannes H. De Kock   ORCID: orcid.org/0000-0002-2468-5572 1 , 2 ,
• Helen Ann Latham 3 ,
• Stephen J. Leslie 4 ,
• Mark Grindle 1 ,
• Sarah-Anne Munoz 1 ,
• Liz Ellis 1 ,
• Rob Polson 1 &
• Christopher M. O’Malley 1  

BMC Public Health volume  21 , Article number:  104 ( 2021 ) Cite this article

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Health and social care workers (HSCWs) have carried a heavy burden during the COVID-19 crisis and, in the challenge to control the virus, have directly faced its consequences. Supporting their psychological wellbeing continues, therefore, to be a priority. This rapid review was carried out to establish whether there are any identifiable risk factors for adverse mental health outcomes amongst HSCWs during the COVID-19 crisis.

We undertook a rapid review of the literature following guidelines by the WHO and the Cochrane Collaboration’s recommendations. We searched across 14 databases, executing the search at two different time points. We included published, observational and experimental studies that reported the psychological effects on HSCWs during the COVID-19 pandemic.

The 24 studies included in this review reported data predominantly from China (18 out of 24 included studies) and most sampled urban hospital staff. Our study indicates that COVID-19 has a considerable impact on the psychological wellbeing of front-line hospital staff. Results suggest that nurses may be at higher risk of adverse mental health outcomes during this pandemic, but no studies compare this group with the primary care workforce. Furthermore, no studies investigated the psychological impact of the COVID-19 pandemic on social care staff. Other risk factors identified were underlying organic illness, gender (female), concern about family, fear of infection, lack of personal protective equipment (PPE) and close contact with COVID-19. Systemic support, adequate knowledge and resilience were identified as factors protecting against adverse mental health outcomes.

Conclusions

The evidence to date suggests that female nurses with close contact with COVID-19 patients may have the most to gain from efforts aimed at supporting psychological well-being. However, inconsistencies in findings and a lack of data collected outside of hospital settings, suggest that we should not exclude any groups when addressing psychological well-being in health and social care workers. Whilst psychological interventions aimed at enhancing resilience in the individual may be of benefit, it is evident that to build a resilient workforce, occupational and environmental factors must be addressed. Further research including social care workers and analysis of wider societal structural factors is recommended.

Peer Review reports

Health and social care workers (HSCWs) continue to play a vital role in our response to the COVID-19 pandemic. It is known that HSCWs exhibit high rates of pre-existing mental health (MH) disorders [ 1 , 2 , 3 ] which can negatively impact on the quality of patient care [ 4 ].

Studies from previous infectious outbreaks [ 5 , 6 ] suggest that this group may be at risk of experiencing worsening MH during an outbreak. Current evidence examining the psychological impact on similar groups [ 7 , 8 , 9 ], suggest that this group may be at risk of experiencing poor MH as a direct result of the COVID-19 pandemic. Compounding the concerns about these data are that HSCWs will be likely to not only be at a higher risk for experiencing MH problems during the pandemic, but also in its aftermath [ 5 ].

There are some specific features of the COVID-19 pandemic that may specifically heighten its potential to impact on the MH of HSCWs.

Firstly, the scale of the pandemic in terms of cases and the number of countries affected has left all with an impression that ‘no-one is safe’. Media reporting of the pandemic has repeatedly focused on the number of deaths in HSCWs and the spread of the disease within health and social care facilities which is likely to have amplified the negative effects on the MH of HSCWs.

Secondly, usual practice has been significantly disrupted and many staff have been asked to work outside of their usual workplace and have been redeployed to higher risk front line jobs.

Finally, the intense focus on personal protective equipment (PPE) is likely to have specifically heightened the impact of COVID-19 on the MH of HSCWs due to the uncertainty surrounding the quantity and quality of equipment, the frequently changing guidance on what PPE is appropriate in specific clinical situations and the uncertainty regarding the absolute risk of transmission posed. While other workers will have been impacted by COVID-19, it is highly likely that the above factors will have disproportionately affected the MH of HSCWs [ 9 , 10 ]. Indeed a British Medical Association survey on the 14th May 2020 during the pandemic showed that 45% of UK doctors are suffering from depression, anxiety, stress, burnout or other mental health conditions relating to, or made worse by, the COVID-19 crisis [ 11 ].

Although evidence based psychological interventions are available for this population [ 12 ], there is a paucity of evidence about interventions for the MH of HSCWs during pandemics. Recent calls to action mandated the need to provide high quality data on the psychological impacts of the COVID-19 pandemic [ 13 , 14 ]. This pandemic has rapidly changed the functioning of society at many levels which suggests that these data are not only needed swiftly, but also with caution and scientific rigour [ 13 , 14 ].

These data are needed in order to equip HSCWs to do their job effectively – high levels of stress and anxiety have been shown to decrease staff morale, increase absenteeism, lower levels of work satisfaction and quality of care [ 6 , 15 ]. It is therefore a priority to understand the psychological needs of our HSCWs in order to provide them with the appropriate tools to mitigate the negative effects of dealing with the COVID-19 pandemic.

While HSCWs have been identified as vulnerable to the negative psychological impact from the current pandemic, they do not form a homogeneous population. It may therefore be appropriate to identify particularly vulnerable groups within the larger population of HSCWs and target psychological support to them. This review seeks to understand whether any group of HSCWs could be confidently excluded from psychological support interventions because they are deemed to be at a low risk. Holmes et al. [ 14 ] have warned that a one-size-fits-all approach to supporting HSCWs might not be effective. This, together with the lack of evidence around tailoring psychological interventions during pandemics [ 1 ], highlights the importance of identifying vulnerable groups, to ensure appropriately personalised interventions are made available.

Aim of the review

The aim of this review is to identify the psychological impact of the COVID-19 pandemic on the health and social care professions, more specifically to identify which sub-groups are most vulnerable to psychological distress and to identify the risk and protective factors associated with this population’s mental health.

This review, looking exclusively at the psychological impact of the COVID-19 pandemic on HSCWs will therefore contribute to informing where mental health interventions, together with organisational and systemic efforts to support this population’s mental health could be focussed in an effort to support psychological well-being [ 14 ]. Rapid but robust gathering of evidence to inform health decision-makers is vital and in circumstances such as these, the WHO recommends rapid reviews [ 16 ].

Search strategy

Planning, conducting and reporting of this study was based on the guidelines for rapid reviews [ 17 ], set by the WHO [ 16 ] and the recent COVID-19 Cochrane Collaboration’s recommendations [ 18 ].

Data sources and searches

Two authors (CoM & RP) searched across a broad range of databases to capture research from potentially relevant fields, including health, mental health and health management. Within the OVID platform of databases Medline, EMBase, HMIC and PsychInfo were searched. Within the EbscoHost platform of databases, CINAHL, Medline, APA PsychInfo, Business Source Elite, Health Source and Academic Search Complete were searched. Beyond the OVID and EbscoHost platforms, SCOPUS, the King’s Fund Library, Social Care Online, PROSPERO and Google Advanced were also searched, making 16 databases searched (14 unique databases and two having been searched twice on separate platforms).

Owing to the rapidly changing landscape of the COVID-19 pandemic, and in an effort to include as many eligible papers as possible, the search strategy was executed on 23 April 2020 and again 2 weeks later on 6 May 2020 using a combination of subject headings and keyword searching (see Additional file 1 ). The bibliographical database was created with EndNote X7™.

Search criteria

The design of the search criteria was intended to draw together research both for this rapid review, and to contribute to the design of a digital mental health intervention to enhance the psychological well-being of HSCWs. The design of the search criteria is discussed in further detail in the Additional file 1 .

Types of participants

Participants were restricted to HSCWs during the COVID-19 pandemic.

Types of studies included

Published observational and experimental studies that reported the psychological effects on HSCWs during the COVID-19 pandemic were included. The study designs included quantitative and qualitative primary studies. Studies relating to previous pandemics and epidemics (such as SARS, MERS, H1N1, H5N1, Zika, Ebola, West Nile Fever) were excluded as these results have been reported elsewhere [ 7 ]. Reviews, theses, position papers, protocol papers, and studies published in languages other than English were excluded.

Screening and selection of studies

Searches were screened according to the selection criteria by JDK. The full text of potentially relevant papers was retrieved for closer examination. The reviewer erred on the side of inclusion where there was any doubt, to ensure no potentially relevant papers were missed. The inclusion criteria were then applied against full text versions of the papers (where available) independently by JDK and HL. Disagreements regarding eligibility of studies were resolved by discussion and consensus. Where the two reviewers were still uncertain about inclusion, the other reviewers (RP, CoM) were asked to provide input to reach consensus.

Data extraction and quality assessment

Relevant data were extracted into structured tables including country, setting, population, study design, number of participants, mental health conditions and their measurement tools and main study results. Where available, we extracted risk factors and protective factors. HL, LE and JDK extracted all the data while JDK checked for accuracy and completeness.

Table  2 presents an overview of the validated tools used per study type to assess study quality and risk of bias. JDK and HL assessed the quality of cross-sectional studies with the Joanna Briggs Institute tool [ 48 ] and JDK assessed their risk of bias using the Evidence Partners [ 49 ] appraisal tool. JDK assessed the risk of bias for the longitudinal study with the Critical Appraisal Skills Programme (CASP) appraisal tool [ 50 ] and the uncontrolled before-after study with the ROBINS – I [ 51 ]. SAM utilised Joanna Briggs Institute tool to assess the qualitative studies [ 38 ] and the Mixed methods appraisal tool (MMAT) [ 41 ] to assess mixed methods studies.

Data synthesis and analysis

Current best practice guided the tabulated and narrative synthesis of the results [ 52 , 53 ]. The studies’ outcomes were categorised according to the psychological impact of COVID-19 on HSCWs of:

general psychological impacts

the risk factors associated with adverse mental health outcomes

the protective factors against adverse mental health outcomes

Previous studies’ logical syntheses [ 6 ] were adapted by organising the risk and protective factors into psychosocial, occupational, sociodemographic and environmental categories. The GRADE method from the Cochrane Collaboration [ 54 ] was used to assess the quality of evidence of outcomes included in this rapid review. Varied study quality, together with study type and outcome heterogeneity precluded performing a meta-analysis.

Patient and public involvement

Some members of the author team are frontline healthcare staff during the COVID-19 pandemic and contributed to the design of the review.

Search results

The 677 records of interest were found from the two searches (429 in search 1 and 529 in search 2). After 148 duplicates were removed, 529 records were screened. Of these, 82 full texts of potentially relevant studies were assessed for eligibility (see Fig.  1 ). Twenty-four published studies met the inclusion criteria for the rapid review.

Prisma Flow Diagram

Study characteristics

The 24 studies included in this review consisted of 18 cross-sectional, 2 mixed methods, 2 qualitative, 1 longitudinal and 1 uncontrolled before-after study. The total number of participants in these studies was 13,731. In the cross-sectional studies, participant numbers ranged between 59 and 2299. Participant numbers in the two mixed method studies were 37 and 222 respectively, whilst the qualitative studies included 10 and 20 participants, respectively. The longitudinal study included 120 participants and the uncontrolled before-after study, 27 participants. See Table  1 for sampling methods within the included papers. The majority of papers utilised non-probability sampling methods, limiting generalisability of findings. One exception was Lai et al., who used region stratified 2-stage cluster sampling.

Eighteen of the studies were from China, of which 8 were based in Wuhan, where the COVID-19 outbreak began. The rest were from America (1), Israel (1), UK (1), Singapore (1), Pakistan (1), multicentre - Singapore & India (1), Global (1). Several validated measures were used to assess anxiety, depression, insomnia, stress and burnout. Table 1 provides an overview of the included studies.

Risk of bias assessment

The quality of the cross-sectional studies was fair, with 16 studies scoring 6 or higher on the JBI appraisal tool and eleven scoring 7 or higher (a score of 7 and above is an indicator of study quality). The majority of the studies indicated a low risk of bias when assessed with the Evidence Partners’ appraisal tool. The uncontrolled before-after study indicated a high risk of bias. The qualitative studies indicated a good level of quality (JBI scores of 9 & 10 respectively) while mixed methods studies showed varied quality. In the cross sectional studies, the most common problem affecting study quality was failure to deal with confounding factors. Failure to locate the researcher culturally or theoretically affected the qualitative papers, whilst the two mixed methods papers’ study quality was affected by lack of explicitly articulated research questions. A summary of the risk of bias and quality assessments are provided in Table 2 .

Psychological toll on healthcare workers

Of the 24 studies included, 22 directly assessed the psychological toll on healthcare workers and all found levels of anxiety, depression, insomnia, distress or Obsessive Compulsive Disorder (OCD) symptoms [ 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 42 , 43 , 44 , 46 , 47 , 58 , 59 , 60 ].

Psychological symptoms were assessed using various validated measures as outlined in Table  3 – the summary of included studies. The most common outcomes assessed were sleep, anxiety and depression. The prevalence of depressive symptoms varied greatly, ranging between 8.9% [ 39 ] to 50.4% [ 31 ]. These findings suggest marked differences in the prevalence of depressive symptoms across the studies. The prevalence of anxiety in cross-sectional studies ranged between 14.5% [ 39 ] to 44.6% [ 31 ]. Sleep was also assessed in several studies. Lai et al. [ 31 ] found the prevalence of sleep disturbances to be 34%, whilst another, nationwide survey in China found that HCWs had significantly worse sleep than the general population [ 29 ].

Risk factors associated with adverse mental health outcomes

Table 3 provides the GRADE evidence profile of the certainty of evidence for the risk factors associated with adverse MH outcomes during the COVID-19 pandemic identified through the review. These risk factors can be grouped into the three thematic areas of i) occupational, ii) psychosocial, iii) environmental.

Occupational factors

Medical hcws.

Two studies showed that medical HCWs (nurses and doctors) had significantly higher levels of MH risk in comparison to non-medical HCWs [ 34 , 47 ]. Zhang et al. [ 47 ] found that medical HCWs had significantly higher levels of insomnia, anxiety, depression, somatization and OCD symptoms in comparison to non-medical HCWs. This was also reflected in a large study in Fujian province, China, in which medical staff had significantly higher anxiety than admin staff [ 34 ]. In contrast, Tan et al. [ 39 ] found that in a population of 470 HCWs in Singapore, the prevalence of anxiety was significantly higher among non-medical HCWs than medical.

Healthcare groups

In three studies nurses were found to be at risk of worse MH outcomes than doctors [ 24 , 26 , 31 ]. One large study in China found nurses were at significant risk of more severe depression and anxiety than doctors [ 31 ]. Another found that nurses had significantly higher financial concerns than doctors and felt significantly more anxious on the ward when compared with other groups. There was no significant difference between professionals regarding stopping work or work overload [ 24 ]. A mixed method paper also showed that nurses had a higher rate of depressive symptoms than doctors. Whilst this was a small sample size, it echoes the findings from larger studies [ 26 ].

With regard to other HCWs, there were two studies which assessed dentists and other dental workers and found them to be at risk of anxiety and elevated distress. Neither study found any difference based on gender or educational level [ 36 , 59 ]. There were no studies comparing dental workers to other HCWs. We did not find any studies that focussed on the primary care workforce or that assessed social care workers.

With regard to seniority, one paper found that having an intermediate technical title was associated with more severe MH symptoms [ 31 ].

Frontline staff/direct contact with COVID-19

Four high-quality studies found being in a ‘frontline’ position or having direct contact with COVID-19 patients was associated with higher levels of psychological distress [ 30 , 31 , 34 , 42 ].

Increased direct exposure to COVID-19 patients increased the mental health risks in health care workers in one study in Wuhan [ 30 ]. This finding is backed by Lai et al. [ 31 ], who found that being a frontline worker was independently associated with more severe depression, anxiety and insomnia scores. In addition, a cross sectional survey of staff in a paediatric centre found that contact with COVID-19 patients was independently associated with increased risk of sleep disturbance [ 42 ]. Lu et al. [ 34 ] found that medical HCWs in direct contact with COVID-19 patients had almost twice the risk of anxiety and depression than non-medical staff with low risk of contact with COVID-19.

There were conflicting results found in two studies. A study in a cancer hospital in Wuhan found burnout frequency to be lower in frontline staff [ 43 ]. The authors identified confounding factors which may have led to this result, but it is of interest as it is one of the only studies that assessed HCWs outside of the acute general medicine setting. Li et al. [ 32 ], also found that frontline nurses had significantly lower levels of vicarious trauma scores than non-frontline workers and the general population.

Personal protective equipment (PPE)

PPE concerns were the most common theme brought up voluntarily in free-text feedback in a study by Chung & Yeung [ 60 ], and a survey in Pakistan revealed that 80% of participants expected provision of PPE [ 40 ]. H.Cai et al. [ 24 ] also found that PPE was protective when adequate, but a risk factor for stress when inadequate. This finding appears to be bolstered by a qualitative study of frontline nurses in Wuhan, which found that physical health and safety was one of their primary needs. This study also reported PPE as a protective factor [ 46 ].

Heavy workload

Longer working time per week was found to be a risk factor in a study by Mo et al. [ 35 ] This, together with increased work intensity or patient load per hour, were themes in a mixed methods study of 37 staff of a clinic in Beijing [ 26 ] and a qualitative study of nurses in China [ 37 ], also suggesting heavy workload as a risk factor.

Psychosocial factors

Fear of infection.

A fear of infection was a highlighted in a qualitative study by Cao et al., (2020, 31), and brought up as a theme in free-text feedback in a cross sectional survey by Chung & Yeung [ 60 ]. Ahmed et al. [ 59 ] found that 87% of dentists surveyed described a fear of being infected with COVID-19 from either a patient or a co-worker.

Concern about family

This was brought up as one of the main stress factors in a study by H.Cai et al. [ 24 ], particularly amongst staff in the 31–40 year age-group. Knowing that their family was safe was also the greatest stress reliever [ 24 ], whilst fear of infecting family was identified in 79.7% of 222 participants in a study in Pakistan [ 40 ]. It was also a theme highlighted in the qualitative data [ 26 , 37 ].

Sociodemographic factors

Younger age.

One Chinese web-based survey which included the general population and HCWs, showed that younger people had significantly higher anxiety and depression scores, but no difference in sleep quality. Conversely, the same study found that HCWs were significantly more likely to have poor sleep quality, but found no difference in anxiety or depressive symptoms based on occupation. The study did not examine the effect of age group on HCWs [ 29 ].

H. Cai et al. [ 24 ] suggested that age was more complex. They found that all age groups had concerns, but that the focus of their anxieties were different (for example: older staff were more likely to be anxious due to exhaustion from long hours and lack of PPE while younger staff were more likely to worry about their families).

Women were found to be at higher risk for depression, anxiety and insomnia by Lai et al. [ 31 ] This was also found to be an independent risk factor for anxiety in another large nationwide Chinese study [ 47 ]. However, a global survey of dentists found no differences based on gender [ 59 ].

Underlying illness

We found two studies which identified that having an underlying organic illness as an independent risk factor for poor psychological outcomes. A study of dentists in Israel found an increase in psychological distress in those with background illnesses as well as an increased fear of contracting COVID-19 and higher subjective overload [ 36 ]. In medical HCWs in China, organic illness was found to be an independent risk factor for insomnia, anxiety, OCD, somatising symptoms and depression in medical HCWs [ 47 ].

Being an only child

This was independently associated with sleep disturbance in paediatric HCWs in Wuhan [ 42 ]. Being an only child was also found to be significantly associated with stress by Mo et al. [ 35 ].

There was also a significant association between physical symptoms and poor psychological outcomes in a large multicentre study based in India and Singapore. It is unclear if this represented somatization or organic illness and the authors suggest the relationship between physical symptoms and psychological aspects was bi-directional [ 27 ].

Environmental factors

Point in pandemic curve.

One longitudinal study carried out in China in a surgical department, found that anxiety and depression scores during the ‘outbreak’ period were significantly higher when compared to a similar group assessed after the outbreak period [ 58 ]. This was a small sample of 120 and only assessed surgical staff, but this longitudinal data was supported by a qualitative study in China which suggested that anxiety peaks at the start of the outbreak and reduces with time [ 37 ].

Living in a rural area was only assessed by one study which showed that it was an independent risk factor for insomnia and anxiety in medical HCWs [ 47 ]. This may reflect a need to further investigate the effect of rurality on psychological wellbeing during this pandemic.

Protective factors against adverse mental health outcomes

The review identified protective factors against adverse mental health outcomes during COVID-19. Table  4 provides the GRADE evidence profile of the certainty of evidence for this. The protective factors can be grouped into the three thematic areas of: i) occupational, ii) psychosocial and iii) environmental.

W. Cai et al. [ 25 ] found that previous experience in a public health emergency (PHE) was protective against adverse mental health outcomes. Staff that had no previous experience were also more likely to have low rates of resilience, and social support.

A small cohort study of 27 surgeons, who were given pre and post training surveys, suggested that training alleviates psychological stress [ 22 ]. Good hospital guidance was identified to relieve stress in a study by H.Cai et al. [ 24 ], and increasing self-knowledge was a coping strategy deployed by staff. Dissemination of knowledge was also mentioned in a qualitative study by Yin & Zeng [ 46 ]; participants described subjective stress reduction after their seniors explained relevant knowledge to them.

Adequate PPE

As mentioned above, PPE was found to be a protective factor when adequate and a risk factor for poor mental health outcomes when deemed to be inadequate [ 24 , 46 ].

One study assessed self-efficacy in dental staff and found that it was a protective factor [ 36 ]. Self-efficacy was also found to improve sleep quality by Xiao et al. [ 44 ], whilst W.Cai et al. [ 25 ] measured resilience using a validated measure and found it to be a protective factor against adverse MH outcomes.

Being in a committed relationship

This was found to be protective by Shacham et al. [ 36 ] This was not directly assessed in other studies.

Safety of family

This had the biggest impact in reducing stress in a cross-sectional study by H. Cai et al. [ 24 ] This was also not assessed in other studies.

Support and recognition from the health care team, government and community was identified as a protective theme in several studies. Social support, measured using the Social Support Rate Scale (SSRS) was found to indirectly affect sleep by directly reducing anxiety and stress and increasing self-efficacy [ 44 ].

Team support was identified as a protective factor in a qualitative study by Sun et al. [ 37 ] Good hospital guidance was also identified as a stress reliever by H. Cai et al. [ 24 ], who found that HCWs expected recognition from the hospital authorities. This was echoed in a qualitative study of nurses in Wuhan where the desire for community concern was a strong need and tightly linked to the need for PPE and knowledge [ 46 ]:

‘ To be honest, I was very apprehensive before coming to the infectious department as support staff, but on the first day here, the head nurse personally explained relevant knowledge such as disinfection and quarantine, and that helped me calm down a lot . ”

“I hope that our society and government pay more attention to lack of personal protective equipment’ [ 46 ] .

As a communicable disease, and now a global public health emergency (PHE), COVID-19 places a unique challenge on our health and social care workforce that will disrupt not just their usual workplace duties but also their social context [ 62 ]. As we adjust to new ways of living and working, HSCWs are likely to continue to face challenges ahead. Our review confirms that the psychological impact of COVID-19 on health care workers is considerable, with significant levels of anxiety, depression, insomnia and distress. Studies revealed a prevalence of depressive symptoms between 8.9–50.4% and anxiety rates between 14.5–44.6% [ 31 , 39 ]. This is in keeping with other reviews and findings from previous viral outbreaks [ 7 , 8 , 63 ]. The majority of studies published to date come from China, particularly Wuhan - the epicentre of COVID-19. There is minimal evidence published to date on the psychological impact on HCWs in Europe or the US, which have been highly impacted by the pandemic. The studies included in this review were predominantly concerned with hospital settings – we found no studies relating to social care staff or primary care staff. This is a concern, as we have increasing evidence that a large proportion of Western deaths are happening in the community and specifically in care homes [ 64 ].

Our review aimed to identify whether there were any groups particularly vulnerable to poor mental health outcomes during COVID-19. We found some evidence that nurses may be at a higher risk than doctors [ 24 , 26 , 31 ]. This is similar to findings which take into account previous viral outbreaks [ 7 ]. Confounding factors were not robustly addressed however, and there were no studies that compared nurses with the primary care workforce or social care workers. There was some evidence that clinical HCWs may be at higher risk of psychological distress than non-clinical HCWs [ 34 , 47 ], but this was not absolute. Tan et al. [ 39 ] found a higher prevalence of anxiety among non-medical HCWs in Singapore. The prevalence of poor MH outcomes varied between countries. Chew et al. [ 27 ] revealed that in data from India and Singapore, there was an overall lower prevalence of anxiety and depression than similar cross-sectional data from China [ 27 , 31 , 39 , 60 ]. This suggests that different contexts and cultures may reveal different findings. It is possible that being at different points in their respective countries’ outbreak curve may have played a part, as there was evidence that this may be influential [ 58 ]. Tan et al. [ 39 ] postulated that the medical HCWs in Singapore had experienced a SARS outbreak in the past and thus were well prepared for COVID-19 both psychologically and in their infection control measures. What we can deduce is that context and cultural factors are likely to play a role, not just cadre or role of healthcare worker. It also highlights the importance of reviewing the evidence as more data emerges from other countries.

Several risk factors emerged, many in keeping with what has been found in other reviews [ 7 , 8 ]. Those with the strongest evidence were inadequate PPE [ 24 , 40 , 46 , 60 ], fear of infection [ 26 , 59 , 60 ] and heavy workload [ 26 , 35 , 37 ]. Consistent with prior outbreak data [ 7 , 63 ], there was also good evidence that close contact with COVID-19 cases was a predictor of higher levels of anxiety, depression and insomnia [ 30 , 31 , 34 , 42 ], although two studies appeared to show conflicting results [ 32 , 43 ]. Studies suggested that being younger in age [ 24 , 29 , 33 ] or being female [ 31 , 47 , 59 ] may be a risk factor, however this should be treated with caution. An alternative explanation for this study’s findings may be greater risk of frontline exposure amongst women, who are predominantly employed in lower status roles within healthcare globally according to the WHO [ 65 ]. It is important to note that respondents to all studies, when disaggregated by gender, were predominantly female and this may have impacted findings. The consistently higher mortality rate and risk of severe COVID-19 disease amongst men would suggest that the full picture regarding gender and MH during this pandemic is incomplete [ 66 , 67 ]. Although other risk factors were also identified, their certainty of evidence was deemed to be low.

The majority of cross-sectional studies focussed on measuring adverse MH outcomes which explains the lack of quantitative data on protective factors or coping mechanisms. Of the studies that did assess this, there were protective factors which were associated with adaptive psychological outcomes. Experience of prior infectious disease outbreaks and training were protective against poor mental health outcomes [ 22 , 24 , 25 , 46 ]. Adequate PPE was a protective factor when adequate and a risk factor when inadequate [ 24 , 46 , 60 ]. There was good evidence that resilience (measured by self-efficacy or resilience scales) was protective against poor mental health outcomes [ 25 , 36 , 44 ]. This is of importance when assessing how to positively contribute to reducing the psychological burden on our health and social care staff. There was strong evidence that community support was a protective factor [ 24 , 37 , 44 , 46 ]. Community support was important in a number of studies, referring to social support as well as recognition and support from the healthcare team, government and wider community [ 24 , 37 , 44 , 46 , 68 ]. Other adaptive behaviours emerged from qualitative data, including gratitude and the ability to find purpose and growth from the situation [ 37 ]. These findings are in keeping with a recent study which identified key domains of risk for burnout in healthcare. They highlighted that being part of a supportive team community is a strong protective factor as are clear values and meaningful work [ 69 ]. They advise that organisational-level interventions creating a healthy workplace are the key to preventing burnout [ 69 ]. This is echoed in a recent systematic review and meta-analysis of the effectiveness of interventions designed to reduce symptoms and prevalence of MH disorders and suicidal behaviour among physicians. This review concluded that, whilst individually directed interventions are associated with some reduction in symptoms of common MH disorders, there needs to be increased focus on organisational-level interventions that improve the work environment [ 2 ].

Whilst our findings showed evidence that occupational and environmental factors at the workplace level played a key role for MH outcomes, there was no mention of wider societal structural issues that have been emerging during this pandemic. Of particular importance is the evidence that black and ethnic minority people of all ages in the global north are at greater risk of contracting and dying from COVID-19 [ 70 , 71 , 72 ]. A recent large study in the US found that non-white HCWs were at increased risk of contracting COVID-19 and were disproportionately affected by inadequate PPE and close exposure to COVID-19 patients [ 3 ]. This suggests wider structural factors are at play and need to be investigated.

The paucity of empirical studies investigating the mental health of social care and primary care staff during the COVID-19 pandemic should also be rectified. With the majority of studies taking place in China, where ageing in place rather than residential care is the norm [ 73 ], it is unsurprising that none investigated care homes, where it is estimated around 40–50% of all deaths related to COVID-19 occur in Europe and the US [ 64 ]. Moreover, there is evidence that front-line HCWs who work in nursing homes are among the highest at risk of contracting the virus [ 3 ]. With the majority of studies taking place in urban hospital settings, and particularly in Wuhan – the epicentre of the outbreak – the generalizability of findings to other settings may be limited, particularly as countries pass through different points in the outbreak curve. However, this review does highlight the considerable psychological impact that COVID-19 has played so far on health care workers and, therefore, adds to the recent calls to take notice of this important issue [ 14 ]. Yet the evidence also suggests that, although predictors for psychological distress exist, these are not absolute and context may play an important role on the manifestation of adverse MH outcomes.

Strengths and limitations

This rapid review has synthesized and discussed the current literature on the psychological impact of the COVID-19 pandemic on health and social care workers. A major limitation was that no empirical studies investigating this impact on social care workers could be found – limiting generalisability to the population reviewed. Recent evidence also suggests that having an ongoing connection to a paid job, may be protective against poor MH outcomes during the pandemic [ 74 ]. It would therefore be useful to compare MH outcomes amongst HCWs, or the general population, who were not actively employed during the pandemic. Unfortunately, none of the studies included this data. Furthermore, job retention schemes have varied widely between countries worldwide, thus limiting the generalisability of findings if this data had been available [ 75 ].

However, to our knowledge, this is the first review investigating this population group in the context of COVID-19, without including prior viral outbreaks in its analysis and synthesis. We see this as a strength because this outbreak is different, and worth assessing in its own right. It has affected every country across the globe and disrupted everyday living in a way no other outbreak has in living memory [ 14 ]. A major strength of our review is that it endeavoured towards greater inclusion, during the rapidly changing COVID-19 landscape, by completing two runs of the search strategy spaced 2 weeks apart. Whilst we adhered to high methodological standards by assessing study quality and risk of bias, together with using the GRADE approach to evaluate the certainty evidence and following best practice principles [ 52 , 53 ] to present a narrative and tabulated synthesis, our review remains a rapid one with further clear limitations. The majority of the studies included in this review, for example, were from China and our selection criteria did not include studies from low-income countries or studies in languages other than English - limiting the generalizability of our findings. Being a rapid review, the protocol was not registered on PROSPERO and only one reviewer was responsible for the initial screening of papers and for several of the quality assessments. Finally, as the current review’s searches were carried out early in the pandemic, it will be valuable to consider emerging research from the global arena in the light of this review’s findings.

This rapid review confirms that front line HCWs are at risk of significant psychological distress as a direct result of the COVID-19 pandemic. Published studies suggest that symptoms of anxiety, depression, insomnia, distress and OCD are found within the healthcare workforce. However, most studies draw only from work in secondary care and none draw from the primary care or social care setting. Published studies so far are predominantly from China (18 out of 24 included studies) and most of these have sampled hospital staff in Wuhan - the epicentre. Findings in this review suggest that the study of different contexts and cultures may reveal different findings and we recommend more research in primary care and social care settings and to monitor rapidly emerging evidence from across the world. This should include analysis of wider societal factors including gender, racial and socio-economic disparities that may influence mental health outcomes in HCWs.

Although risk factors did emerge that were in keeping with evidence from other infectious disease outbreaks, our findings were not absolute. This review suggests that nurses may be at higher risk of adverse MH outcomes during this pandemic, but there were no studies comparing them with social care workers or the primary care workforce. Other risk factors that recurred in the data were heavy workload, lack of PPE, close contact with COVID-19, being female and underlying organic illness. Inconsistencies in findings and lack of data on staff outside hospital settings, suggest that targeting a specific group within health and social care staff with psychological interventions may be misplaced – as both presence of psychological distress and risk factors are spread across the healthcare workforce, rather than associated with particular sub-groups.

A recent call to action for mental health science during COVID-19 recommends research be undertaken to identify interventions that can be delivered under pandemic conditions to mitigate deteriorations in psychological well-being and support mental health. This call to action advised that personalised psychological approaches are likely to be a key [ 14 ]. Data from this review suggests that interventions which bolster psychological resilience may be of benefit because this was found to protect against adverse mental health outcomes. Due to the nature of the pandemic which prevents face-to-face interventions, this is likely to be digitally based. A recent systematic review, pre-dating COVID-19, suggested that individualised interventions can have modest effect on reducing adverse mental health outcomes amongst physicians [ 2 ]. However, our findings suggest that occupational and environmental factors in the workplace play a key role as risk factors and protective factors for mental health outcomes during this pandemic. Heavy workload, proximity to COVID-19 and inadequate PPE were risk factors for poor mental health, whereas good knowledge of COVID-19, a supportive work environment and adequate PPE were protective factors. It would appear from our findings that adequate PPE may be protective not just against infection, but also against adverse mental health outcomes. Individually targeted digital interventions are unlikely to address these factors [ 2 ]. We postulate that strengthening psychological resilience in a personalised approach may be effective in protecting our health and social care workers from adverse mental health outcomes but this must not defer responsibility from wider organisations and systems. We suggest that a holistic approach to HCWs psychological wellbeing is needed that includes personalised interventions alongside necessary structural changes to create a healthy, safe and supportive work environment. Further research including social care workers and analysis of wider societal structural factors is recommended.

Availability of data and materials

The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

Connor-Davidson Resilience Scale

Centre for Epidemiologic Studies Depression Scale (CES-D)

Coronavirus disease 2019

Depression, Anxiety and Stress Scale

Generalised Anxiety Disorder Questionnaire

The Grades of Recommendation, Assessment, Development and Evaluation Working Group

Generalised self-efficacy scale

Hamilton Anxiety Rating Scale

Hamilton Depression Rating Scale

Healthcare workers

Health and social care workers

Impact of Event Scale

Insomnia Severity Index

Maslach Burnout Inventory (MBI)

• Mental health

Public Health Emergency

Patient Health Questionnaire-4

Patient Health Questionnaire

Personal protective equipment

Pittsburgh Sleep Quality Index

Zung Self-Rating Anxiety Scale

The Stanford Acute Stress Reaction questionnaire

Symptom checklist depression scale

The Symptom Checklist-90-R

Zung Self-Rating Depression Scale

Short Form Health Survey (SF-36)

Stress Overload Scale

Social Support Rating Scale

World Health Organisation

Gold JA. Covid-19: adverse mental health outcomes for healthcare workers. BMJ. 2020;369:m1815 https://doi-org.knowledge.idm.oclc.org/10.1136/bmj.m1815 .

Article   PubMed   Google Scholar  

Petrie K, Crawford J, Baker STE, Dean K, Robinson J, Veness BJ, et al. Interventions to reduce symptoms of common mental disorders and suicidal ideation in physicians: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6(3):225–34 https://doi.org/10.1016/S2215-0366(18)30509-1 .

Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Health. 2020;5(9):e475–83. https://doi.org/10.1016/S2468-2667(20)30164-X .

Article   PubMed   PubMed Central   Google Scholar  

Tawfik DS, Scheid A, Profit J, Shanafelt T, Trockel M, Adair KC, et al. Evidence relating health care provider burnout and quality of care: a systematic review and meta-analysis. Ann Intern Med. 2019;171(8):555–67. https://doi.org/10.7326/M19-1152 .

Maunder R, Lancee W, Balderson K, Bennett J, Borgundvaag B, Evans S, et al. Long-term psychological and occupational effects of providing hospital healthcare during SARS outbreak. Emerging Infect Dis. 2006;12(12):1924–32. https://doi.org/10.3201/eid1212.060584 .

Article   Google Scholar  

Brooks SK, Dunn R, Amlôt R, Rubin GJ, Greenberg N. A systematic, thematic review of social and occupational factors associated with psychological outcomes in healthcare employees during an infectious disease outbreak. J Occup Environ Med. 2018;60(3):248–57. https://doi.org/10.1097/JOM.0000000000001235 .

Kisely S, Warren N, McMahon L, Dalais C, Henry I, Siskind D. Occurrence, prevention, and management of the psychological effects of emerging virus outbreaks on healthcare workers: rapid review and meta-analysis. BMJ. 2020;369:m1642. https://doi-org.knowledge.idm.oclc.org/10.1136/bmj.m1642 .

Spoorthy MS, Pratapa SK, Mahant S. Mental health problems faced by healthcare workers due to the COVID-19 pandemic–a review. Asian J Psychiatr. 2020;51:102119. https://doi.org/10.1016/j.ajp.2020.102119 .

Reger MA, Piccirillo ML, Buchman-Schmitt J. COVID-19, mental health, and suicide risk among health care workers: looking beyond the crisis. J Clin Psychiatry. 2020;81(5). https://doi.org/10.4088/JCP.20com13381 .

Neto MLR, Almeida HG, Esmeraldo JD, Nobre CB, Pinheiro WR, de Oliveira C, et al. When health professionals look death in the eye: the mental health of professionals who deal daily with the 2019 coronavirus outbreak. Psychiatry Res. 2020;288:112972. https://doi.org/10.1016/j.psychres.2020.112972 .

Article   PubMed   PubMed Central   CAS   Google Scholar  

British Medical Association. The mental health and wellbeing of the medical workforce – now and beyond COVID-19. 2020. Available from URL: https://www.bma.org.uk/media/2475/bma-covid-19-and-nhs-staff-mental-health-wellbeing-report-may-2020.pdf .

Google Scholar  

Melnyk BM, Kelly SA, Stephens J, Dhakal K, McGovern C, Tucker S, et al. Interventions to improve mental health, well-being, physical health, and lifestyle behaviors in physicians and nurses: a systematic review. Am J Health Promot. 2020:089011712092045 https://doi-org.knowledge.idm.oclc.org/10.1177/0890117120920451 .

United Nations. Policy Brief: COVID-19 and the need for action on mental health. 2020. Available from URL: https://unsdg.un.org/sites/default/files/2020-05/UN-Policy-Brief-COVID-19-and-mental-health.pdf .

Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry. 2020;7(6):547–60 https://doi.org/10.1016/S2215-0366(20)30168-1 .

Brooks SK, Gerada C, Chalder T. Review of literature on the mental health of doctors: are specialist services needed? J Ment Health. 2011;20(2):146–56 https://doi-org.knowledge.idm.oclc.org/10.3109/09638237.2010.541300 .

Tricco AC, Langlois EV, Straus SE. Rapid reviews to strengthen health policy and systems: a practical guide. Geneva: World Health Organisation; 2017. Available from URL: https://apps.who.int/iris/bitstream/handle/10665/258698/9789241512763-eng.pdf;sequence=1 .

Khangura S, Konnyu K, Cushman R, Grimshaw J, Moher D. Evidence summaries: the evolution of a rapid review approach. Syst Rev. 2012;1(1):10. https://doi.org/10.1186/2046-4053-1-10 .

Garritty C, Gartlehner G, Kamel C, King V, Nussbaumer-Streit B, Stevens A., et al. Cochrane rapid reviews. interim guidance from the Cochrane Rapid Reviews Methods Group. 2020. Available from URL: https://methods.cochrane.org/rapidreviews/sites/methods.cochrane.org.rapidreviews/files/public/uploads/cochrane_rr_-_guidance-23mar2020-final.pdf .

Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, et al. Chapter 7: systematic reviews of etiology and risk. In: Joanna Briggs Institute Reviewer's Manual The Joanna Briggs Institute; 2017. p. 2019–05.

Evidence Partners. Tool to Assess Risk of Bias. Contributed by the CLARITY Group at McMaster University: McMaster University; [Available from: https://www.evidencepartners.com/resources/methodological-resources/ .

Ahmed MA, Jouhar R, Ahmed N, Adnan S, Aftab M, Zafar MS, et al. Fear and practice modifications among dentists to combat novel coronavirus disease (COVID-19) outbreak. Int J Environ Res Public Health. 2020;17(8):2821.

Article   CAS   PubMed Central   Google Scholar  

Balakumar C, Rait J, Montauban P, Zarsadias P, Iqbal S, Fernandes R. COVID-19: are frontline surgical staff ready for this? Br J Surg. 2020;107(7):e195. https://doi.org/10.1002/bjs.11663 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Jüni P, Loke Y, Pigott T, Ramsay C, Regidor D, Rothstein H, et al. Risk of bias in non-randomized studies of interventions (ROBINS-I): detailed guidance. 2016.

Cai H, Tu B, Ma J, Chen L, Fu L, Jiang Y, et al. Psychological impact and coping strategies of frontline medical staff in Hunan between January and march 2020 during the outbreak of coronavirus disease 2019 (COVID-19) in Hubei, China. Med Sci Monit. 2020;26:e924171. https://doi.org/10.12659/MSM.924171 .

Cai W, Lian B, Song X, Hou T, Deng G, Li H. A cross-sectional study on mental health among health care workers during the outbreak of Corona virus disease 2019. Asian J Psychiatr. 2020;51:102111. https://doi.org/10.1016/j.ajp.2020.102111 .

Cao J, Wei J, Zhu H, Duan Y, Geng W, Hong X, et al. A study of basic needs and psychological wellbeing of medical workers in the fever clinic of a tertiary general hospital in Beijing during the COVID-19 Outbreak. Psychother Psychosom. 2020;89(4):252–4. https://doi.org/10.1159/000507453 .

Chew NW, Lee GK, Tan BY, Jing M, Goh Y, Ngiam NJH, et al. A multinational, multicentre study on the psychological outcomes and associated physical symptoms amongst healthcare workers during COVID-19 outbreak. Brain Behav Immun. 2020;88:559–65. https://doi.org/10.1016/j.bbi.2020.04.049 .

Chung JPY, Yeung WS. Staff Mental Health Self-Assessment During the COVID-19 Outbreak. East Asian Arch Psychiatry. 2020;30(1):34.

Article   CAS   PubMed   Google Scholar  

Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 2020;288(112954):1–6. https://doi.org/10.1016/j.psychres.2020.112954 .

Kang L, Ma S, Chen M, Yang J, Wang Y, Li R, et al. Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: a cross-sectional study. Brain Behav Immun. 2020;87:11–7. https://doi.org/10.1016/j.bbi.2020.03.028 .

Lai J, Ma S, Wang Y, Cai Z, Hu J, Wei N, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3(3):e203976. https://doi.org/10.1001/jamanetworkopen.2020.3976 .

Li Z, Ge J, Yang M, Feng J, Qiao M, Jiang R, et al. Vicarious traumatization in the general public, members, and non-members of medical teams aiding in COVID-19 control. Brain Behav Immun. 2020;88:916–9. https://doi.org/10.1016/j.bbi.2020.03.007 .

Liang Y, Chen M, Zheng X, Liu J. Screening for Chinese medical staff mental health by SDS and SAS during the outbreak of COVID-19. J Psychosom Res. 2020;133:110102. https://doi.org/10.1016/j.jpsychores.2020.110102 .

Lu W, Wang H, Lin Y, Li L. Psychological status of medical workforce during the COVID-19 pandemic: a cross-sectional study. Psychiatry Res. 2020;288:112936. https://doi.org/10.1016/j.psychres.2020.112936 .

Mo Y, Deng L, Zhang L, Lang Q, Liao C, Wang N, et al. Work stress among Chinese nurses to support Wuhan for fighting against the COVID-19 epidemic. J Nurs Manage. 2020;28:1002–9. https://doi.org/10.1111/jonm.13014 .

Shacham M, Hamama-Raz Y, Kolerman R, Mijiritsky O, Ben-Ezra M, Mijiritsky E. COVID-19 factors and psychological factors associated with elevated psychological distress among dentists and dental hygienists in Israel. Int J Environ Res Public Health. 2020;17(8):2900. https://doi.org/10.3390/ijerph17082900 .

Sun N, Wei L, Shi S, Jiao D, Song R, Ma L, et al. A qualitative study on the psychological experience of caregivers of COVID-19 patients. Am J Infect Control. 2020;48(6):592–8. https://doi.org/10.1016/j.ajic.2020.03.018 .

Lockwood C, Munn Z, Porritt K. Qualitative research synthesis: methodological guidance for systematic reviewers utilizing meta-aggregation. Int J Evid Based Healthc. 2015;13(3):179–87. https://doi.org/10.1097/XEB.0000000000000062 .

Tan BY, Yeo LL, Sharma VK, Chew NW, Jing M, Goh Y, et al. Psychological impact of the COVID-19 pandemic on health care workers in Singapore. Ann Intern Med. 2020;173(4):317–20. https://doi.org/10.7326/M20-1083 .

Urooj U, Ansari A, Siraj A, Khan S, Tariq H. Expectations, fears and perceptions of doctors during Covid-19 pandemic. Pak J Med Sci. 2020;36:S37–42. https://doi.org/10.12669/pjms.36.COVID19-S4.2643 .

Bartlett G, Vedel I, Hong QN, Pluye P, Rousseau M, Fàbregues S, et al. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf. 2018;34(4):285–91. https://doi.org/10.3233/EFI-180221 .

Wang S, Xie L, Xu Y, Yu S, Yao B, Xiang D. Sleep disturbances among medical workers during the outbreak of COVID-2019. Occup Med (Lond ). 2020;70(5):364–9. https://doi-org.knowledge.idm.oclc.org/10.1093/occmed/kqaa074 .

Wu Y, Wang J, Luo C, Hu S, Lin X, Anderson AE, Bruera E, Yang X, Wei S, Qian Y. A comparison of burnout frequency among oncology physicians and nurses working on the front lines and usual wards during the COVID-19 epidemic in Wuhan, China. J Pain Symptom Manage. 2020;60(1):e60–5. https://doi.org/10.1016/j.jpainsymman.2020.04.008 .

Xiao H, Zhang Y, Kong D, Li S, Yang N. The effects of social support on sleep quality of medical staff treating patients with coronavirus disease 2019 (COVID-19) in January and February 2020 in China. Med Sci Monit. 2020;26:e923549. https://doi.org/10.12659/MSM.923549 .

Critical Appraisal Skills Programme. Cohort Study Checklist: Critical Appraisal Skills Programme (CASP); [Available from: https://casp-uk.net/casp-tools-checklists/ .

Yin X, Zeng L. A study on the psychological needs of nurses caring for patients with coronavirus disease 2019 from the perspective of the existence, relatedness, and growth theory. Int J Nurs Sci. 2020;7(2):157–60. https://doi.org/10.1016/j.ijnss.2020.04.002 .

Zhang W, Wang K, Yin L, Zhao W, Xue Q, Peng M, et al. Mental health and psychosocial problems of medical health workers during the COVID-19 epidemic in China. Psychother Psychosom. 2020;89(4):1–9. https://doi.org/10.1159/000507639 .

Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis [Adelaide]: Joanna Briggs Institute; 2020. https://doi.org/10.46658/JBIMES-20-08 .

Evidence Partners, CLARITY Group at McMaster University. Methodological resources: tools to assess risk of bias. 2020; Available from URL: https://www.evidencepartners.com/resources/methodological-resources/ .

CASP. Cohort Study Checklist. 2020. Available from URL: https://casp-uk.net/casp-tools-checklists/ .

Sterne JAC, Hernán M, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ. 2016;i4919:355. https://doi.org/10.1136/bmj.i4919 .

Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC Methods Programme 2006. Available from URL: https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/fhm/dhr/chir/NSsynthesisguidanceVersion1-April2006.pdf .

Campbell M, Katikireddi SV, Sowden A, McKenzie JE, Improving Conduct TH. Reporting of narrative synthesis of quantitative data (ICONS-quant): protocol for a mixed methods study to develop a reporting guideline. BMJ Open. 2018;8(2):e020064. https://doi.org/10.1136/bmjopen-2017-020064 .

Article   PubMed Central   Google Scholar  

Schünemann HJ, Vist GE, Higgins JP, Santesso N, Deeks JJ, Paul Glasziou P, et al. Chapter 15: Interpreting results and drawing conclusions. In: Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane handbook for systematic reviews of interventions. 2nd ed; 2019. p. 403–31. https://doi.org/10.1002/9781119536604.ch15 .

Chapter   Google Scholar  

Cao J, Wei J, Zhu H, Duan Y, Geng W, Hong X, Jiang J, Zhao X, Zhu B. A study of basic needs and psychological wellbeing of medical workers in the fever clinic of a tertiary general hospital in Beijing during the COVID-19 outbreak. Psychother Psychosom. 2020;89(4):252–54. https://doi.org/10.1159/000507453 .

Liang Y, Chen M, Zheng X, Liu J. Screening for Chinese medical staff mental health by SDS and SAS during the outbreak of COVID-19. J Psychosom Res. 2020;110102:133.

Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health. 2020;17(5):1729.

Xu J, Xu Q, Wang C, Wang J. Psychological status of surgical staff during the COVID-19 outbreak. Psychiatry Res. 2020;288:112955. https://doi.org/10.1016/j.psychres.2020.112955 .

Ahmed MA, Jouhar R, Ahmed N, Adnan S, Aftab M, Zafar MS, et al. Fear and practice modifications among dentists to combat novel coronavirus disease (COVID-19) outbreak. Int J Environ Res Public Health. 2020;17(8):2821. https://doi.org/10.3390/ijerph17082821 .

Chung JP, Yeung WS. Staff mental health self-assessment during the COVID-19 outbreak [Letter to editor]. East Asian Arch Psychiatry. 2020;30(1):34. https://doi.org/10.12809/eaap2014 .

Cai W, Lian B, Song X, Hou T, Deng G, Li H. A cross-sectional study on mental health among health care workers during the outbreak of Corona virus disease 2019. Asian J Psychiatr. 2020;102111:51.

Markwell A, Mitchell R, Wright AL, Brown AF. Clinical and ethical challenges for emergency departments during communicable disease outbreaks: can lessons from Ebola virus disease be applied to the COVID-19 pandemic? Emerg Med Australas. 2020;32(3):520–4 https://doi-org.knowledge.idm.oclc.org/10.1111/1742-6723.13514 .

Grace SL, Hershenfield K, Robertson E, Stewart DE. The occupational and psychosocial impact of SARS on academic physicians in three affected hospitals. Psychosomatics. 2005;46(5):385–91 https://doi.org/10.1176/appi.psy.46.5.385 .

Comas-Herrera A, Zalakain J, Litwin C, Hsu AT, Lane N, Fernández J. Mortality associated with COVID-19 outbreaks in care homes: early international evidence. 2020. Available from URL: https://ltccovid.org/2020/04/12/mortality-associated-with-covid-19-outbreaks-in-care-homes-early-international-evidence/ .

Boniol M, McIsaac M, Xu L, Wuliji T, Diallo K, Campbell J. Gender equity in the health workforce: analysis of 104 countries. 2019. Available from URL: https://www.who.int/hrh/resources/gender_equity-health_workforce_analysis/en/ .

Ortolan A, Lorenzin M, Felicetti M, Doria A, Ramonda R. Does gender influence clinical expression and disease outcomes in COVID-19? A systematic review and meta-analysis. Int J Infect Dis. 2020;99:496–504. https://doi.org/10.1016/j.ijid.2020.07.076 .

Pérez-López FR, Tajada M, Savirón-Cornudella R, Sánchez-Prieto M, Chedraui P, Terán E. Coronavirus disease 2019 and gender-related mortality in European countries: A meta-analysis. Maturitas. 2020;141:59–62 https://doi.org/10.1016/j.maturitas.2020.06.017 .

Al Knawy BA, Al-Kadri H, Elbarbary M, Arabi Y, Balkhy HH, Clark A. Perceptions of postoutbreak management by management and healthcare workers of a Middle East respiratory syndrome outbreak in a tertiary care hospital: a qualitative study. BMJ Open. 2019;9(5):e017476. https://doi.org/10.1136/bmjopen-2017-017476 .

Montgomery A, Panagopoulou E, Esmail A, Richards T, Maslach C. Burnout in healthcare: the case for organisational change. BMJ. 2019;366:l4774 https://doi.org/10.1136/bmj.l4774 .

Aldridge RW, Lewer D, Katikireddi SV, Mathur R, Pathak N, Burns R, et al. Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data. Wellcome Open Res. 2020;5:88. https://doi.org/10.12688/wellcomeopenres.15922.2 .

Tai DB, Shah A, Doubeni CA, Sia IG, Wieland ML. The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clin Infect Dis. 2020. https://doi-org.knowledge.idm.oclc.org/10.1093/cid/ciaa815 .

APM Research Lab. The color of coronavirus: COVID-19 deaths by race and ethnicity in the US. 2020. Available from URL: https://www.apmresearchlab.org/covid/deaths-by-race .

Zhang X, Clarke CL. Rhynas SJ. A thematic analysis of Chinese people with dementia and family caregivers’ experiences of home care in China. Dementia (London, England). 2019:147130121986146 https://doi-org.knowledge.idm.oclc.org/10.1177/1471301219861466 .

Burchell B, Wang S, Kamerāde D, Bessa I, Rubery J. Cut hours, not people: no work, furlough, short hours and mental health during the COVID-19 pandemic in the UK. 2020. Available from URL: https://www.cbr.cam.ac.uk/fileadmin/user_upload/centre-for-business-research/downloads/working-papers/wp521.pdf .

Gentilini U, Almenfi M, Orton I, Dale P. Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures. 2020. Available from URL: https://openknowledge.worldbank.org/handle/10986/33635 .

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De Kock, J.H., Latham, H.A., Leslie, S.J. et al. A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being. BMC Public Health 21 , 104 (2021). https://doi.org/10.1186/s12889-020-10070-3

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Impact of COVID-19 pandemic on chronic diseases care follow-up and current perspectives in low resource settings: a narrative review

Ginenus fekadu, firomsa bekele, tadesse tolossa, getahun fetensa, motuma getachew, lemessa assefa, melkamu afeta, waktole demisew, dinka dugassa, dereje chala diriba, busha gamachu labata.

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Address correspondence to: Ginenus Fekadu, School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong. Tel: +85267623675; E-mail: [email protected]

Received 2021 Apr 2; Accepted 2021 Jun 8; Collection date 2021.

Coronavirus is a respiratory disease that spreads globally. The severity and mortality risk of the disease is significant in the elderly, peoples having co-morbidities, and immunosuppressive patients. The outbreak of the pandemic created significant barriers to diagnosis, treatment and follow-up of chronic diseases. Delivering regular and routine comprehensive care for chronic patients was disrupted due to closures of healthcare facilities, lack of public transportation or reductions in services. The purpose of this narrative review was to update how patients with chronic care were affected during the pandemic, healthcare utilization services and available opportunities for better chronic disease management during the pandemic in resources limited settings. Moreover, this review may call to the attention of concerned bodies to make decisions and take measures in the spirit of improving the burden of chronic diseases by forwarding necessary recommendations for possible change and to scale up current intervention programs.

Keywords: COVID-19, chronic diseases, follow-up care, impact, low resource settings

Corona virus disease 2019 (COVID-19) is a respiratory disease caused by a novel coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [ 1 , 2 ]. It is a big family of viruses that have been obtained since 1965 and currently COVID-19 have been emerged to infect humans. These viruses have three genotypes of alpha, beta, and gamma. This virus is named a zoonotic disease since it originated from animals and birds [ 3 ]. Besides increased fatality rates of the disease, coronavirus has a great impact on the mental health of the community and healthcare workers as the result of its fears [ 4 ]. Globally, chronic diseases are the leading cause of disability and death [ 5 , 6 ]. Hypertension, diabetes, asthma, chronic obstructive pulmonary disease (COPD) and cancer are among the top comorbidities with COVID-19 [ 7 - 9 ].

COVID-19 can infect people of different ages, particularly affecting the elderly age group. In addition, people with underlying co-morbidity and immunosuppressive appeared to be more susceptible to severely ill and a higher risk of death [ 10 - 13 ]. World Health Organization (WHO) has recommended that patients with co-morbidity and weekend immunity should be better protected from infection without discrimination. Irrespective of the disease prognosis, patients presenting with different co-morbidities have a high risk for the severity and complications of the disease [ 13 - 15 ].

The COVID-19 pandemic affected the processes of routine comprehensive care for chronic patients due to disrupted delivery care [ 16 ]. In person physical face-to-face consultations had ceased due to due to government restriction, greater instilled fear and focus shifted toward COVID care. In addition, patients have less chance for community-based support and care [ 16 , 17 ]. Worldwide, the pandemic adversely affected clinical decision-making by limiting laboratory testing and physical examination [ 18 , 19 ]. During the outbreak of the pandemic hospitalization rate, emergency department visits and inpatient clinic visits of chronic diseases were significantly reduced [ 17 ].

This disruption of care has a long-lasting impact on chronic health outcomes that likely surpass the duration of the COVID-19 pandemic. However, the questions of why patients with chronic diseases are more vulnerable to SARS-CoV-2, and what interventions should be taken to reduce the risks are open. An evidence-based update on the impact of COVID 19 on chronic illness patients plays a paramount role for the implementation and evaluation of treatment strategy for patients having chronic diseases in low- and middle-income countries (LMICs). The purpose of this narrative review was to update how patients with chronic care were affected during the pandemic, healthcare utilization services, magnitude of factors and the ways forward for better chronic disease management during Covid-19 in resources limited settings.

Global burden of chronic diseases

The majority of the chronic diseases are silent killers. According to the WHO report of 2018, the mortality of 63% was recorded related to chronic diseases like heart disease, diabetes, cancer, and respiratory disease [ 20 , 21 ]. Currently, the worldwide mortality from non-communicable diseases (NCDs) remains unacceptably high. By 2030, NCDs will contribute to three-quarters of all worldwide deaths [ 22 ].

The impact of these chronic diseases is also becoming increasing in low-income countries. Over 80% of heart disease and diabetes deaths, and almost 90% of deaths from chronic obstructive pulmonary disease occur in LMICs. Heart disease is the primary global cause of mortality, and it was estimated that more than 17.3 million deaths per year [ 23 ]. According to WHO, from a mortality rate of 38 million people reported per year from chronic disease, about 14 million deaths were occurring in the age ranges of 30-70 years, of which 85% is in developing countries.

The presence of at least one comorbidity is estimated to be 20%-51% of COVID-19 patients, while the proportion increases to 50-80% in patients with severe conditions [ 8 , 9 , 24 ].

COVID-19 patients presented with hypertension, diabetes, and coronary heart diseases are more likely to be progressed to the severe conditions [ 25 , 26 ]. COVID-19 patients having cardiovascular diseases (CVDs) are associated with a higher risk of mortality [ 27 ]. Routine care for chronic diseases during the pandemic is the most challenging [ 15 ].

Chronic diseases like HIV, diabetes and kidney diseases are immunosuppressing cases, making patients are more vulnerable to infections. These patients with the COVID-19 are less likely to be cured [ 28 , 29 ]. COVID-19 is one of the leading causes of heart disease and responsible for about 5% of the cases of acute heart failure. Therefore, the mortality rate of COVID-19 patients with a history of CVD is high [ 12 , 30 ].

Patients with asthma and chronic obstructive pulmonary disease (COPD) are more likely to have different risks of severe COVID-19, which may be related to different ACE2 receptor activation [ 24 ]. For better outcomes and monitoring, chronic disease patients must receive effective and timely access [ 31 ]. Additionally, patients having chronic disease require continuous follow-up to manage their disease [ 15 ].

Psychological impacts of COVID-19

“Coronaphobia”, which is the fear of COVID-19 has become an emerging issue among different communities and healthcare workers [ 4 ]. Stress is one of the mental health disorders that occurred as the result of the COVID-19 outbreak. Society has developed fears for themselves and their families, manifesting feelings of helplessness, boredom, loneliness and depression [ 32 - 34 ].

Mental health impacts such as anxiety, stress, and depression were common as the results of the COVID-19 pandemic [ 35 , 36 ]. It was estimated that about of 80% patients’ mental health was affected during the pandemic [ 15 ]. Lack of appropriate treatments for the virus has also increased anxiety. In the majority of the patients, these anxiety symptoms do not reach diagnostic thresholds for a DSM-5 [ 37 ].

The presence of anxiety, worry, uncertainties and stressors in the community can result in long-term consequences, including deterioration of social networks, stigma, possible higher emotional state and other negative outcomes [ 32 - 34 , 37 ]. COVID-19 could also possibly increase psychosis, mood disorder, sleep disturbance, phobia and panic disorder [ 37 , 38 ].

Compared to the general population, patients presenting with psychological co-morbidity are more likely to be affected by different negative mental outcomes like post-traumatic stress disorder, depression, and mood disorders [ 39 ]. Reducing media that raises the issues of coronavirus is recommended to decrease the risk of psychological stress.

The COVID-19 has also increased stigma against people of certain ethnic backgrounds and peoples suspected to have contact with COVID-19 patients. The stigma could undermine social relationships and increase social isolation. As the result peoples hide their disease due to fear of stigma, prevent people from seeking health care immediately, and are disappointed to adopt healthy behaviors [ 32 - 34 , 40 , 41 ].

In one study, COVID-19 had caused an abnormal psychological impact in 22.8% (95% CI: 18.6-27.1) of chronic disease patients [ 42 ]. Patients who had no social support and living alone were more likely to have psychological problems compared to those who had good social support. To prevent COVID-19 impact on those who had no social support and living alone, behavioral therapy like relaxation exercises, counseling and entertainment are beneficial [ 42 ].

Economic impacts of COVID-19 on the healthcare system

Globally, the COVID-19 pandemic has affected the health care budget resource [ 43 ]. This pandemic resulted in the stigmatization of affected individuals, authority figures, and health care professionals [ 44 ]. The social discrimination of infected peoples hindered international trade, finance and relationships, instigating further unrest [ 4 ].

Currently, COVID-19 becomes one cause of the economic crisis besides being declared as a public health emergency. Containment and mitigation measures are needed to limit the economic shutdown especially in LMICs where there is lower health care capacity, shallower financial markets, less fiscal space and poorer management [ 45 ].

Chronic diseases are influenced by a range of individual, social and economic factors, including our perceptions and behavior. Thus, due to the silent nature of the diseases, NCDs tend to be easily overlooked by individuals and policymakers [ 46 ]. As COVID-19 became spreading, the physicians have delayed in the management of chronic disease due to the fear of pandemics [ 4 , 43 , 45 ].

The healthcare costs from NCDs are high and projected to increase. Significant costs to individuals, families, businesses, governments, and health systems add up to major economic impacts. Cardiovascular disease, stroke, and diabetes cause billions of dollars per year [ 20 ]. Healthcare systems in LMICs are mainly affected by COVID-19 due to the unorganized health care system. Before the COVID-19 pandemic, healthcare systems in LMICs faced considerable challenges in providing high-quality, affordable, and universally accessible care. These health systems had limited financial resources, inadequate health care providers, and inadequate drugs [ 39 , 43 , 45 , 47 ].

From all global regions, the magnitude of infectious diseases like tuberculosis and HIV/AIDS is found to be high in Africa. Limited health care services, high dependency, weak economic systems to sustain health and lockdown costs are some factors responsible for higher risk of harm [ 48 , 49 ]. Hence, continued care for chronic disease patients is paramount agenda to decrease the mortality and psychological impacts despite the pandemic [ 15 ].

The impact of COVID-19 on follow-up and care

Patients with chronic diseases require regular disease management and close follow-up to reduce risks of adverse health outcomes [ 17 ]. Resources at different resources are re-allocated from chronic disease prevention, diagnosis, management, and rehabilitation during the outbreak. The lockdown of different services also decreased referral, access and hospitalization resulting in inadequate ongoing care for chronic conditions among needy patients [ 16 , 17 ].

As the results of COVID-19 on the health care system, patients needing chronic follow-up postponed their follow-up. Medical mistrust results in inappropriate use of resources and inadequate management of the disease. Moreover, medical mistrust can result in stigma and decreases their adherence [ 37 , 38 ].

Different common reactions like a decrease in health care services other than the pandemic disease negatively affected the outcomes of chronic diseases [ 36 ]. Even patients following their medications, inappropriate counseling information leads to irrational drug use and drug interactions [ 50 ].

Noncompliance with drug therapy is the most public health challenge. In Europe, it has been estimated that 9% of cardiovascular events can be attributed to non-adherence [ 51 ]. Non-adherence is likely to happen due to the far distance of the patients from the health facilities to take their medications [ 52 , 53 ]. As the results of distant health facilities, patients with chronic diseases find it difficult to follow up with their therapy [ 52 ]. Therefore, regular visits and evaluations of the patients becoming more challenging and difficult to practice [ 39 ].

Comorbidities in chronic disease patients are great challenges in public health among developing countries [ 54 ]. The shortage of medicines, diagnostic equipment and the absence of treatment guidelines are major challenges [ 46 ]. Routine health care reports are incomplete and erratic. There is insufficient knowledge of the epidemiological transition of chronic diseases. For severely ill patients, lockdown may impact patients requiring regular treatment and follow-up [ 55 ]. Suggested solutions like social distancing and lockdowns to combat the pandemic also greatly affected patients having chronic diseases who are difficult to access to hospitals for needing support [ 43 ].

Expert opinion

Worldwide, the COVID-19 is placing a big problem on health care systems. Patients with low immunity systems like chronic disease patients are more prone to higher morbidity and mortality. As a result of the unorganized health care system in developing countries, the management of the COVID-19 pandemic in these countries need special attention.

Patients with chronic diseases contribute to a considerable proportion of the whole population, and appropriate management of comorbidities is of great significance in mitigating the COVID-19. Efforts should be made by healthcare systems, medical institutions, and government during the difficult situation.

Giving strong focus and providing critical data on unknown effects of the pandemic is indispensable to formulate evidence-based approaches to its management. Enhancing the knowledge of the disease and legal approaches, can increase healthy behaviors and reduce the incidence of major chronic diseases in LMICs. There is a need to re-orient the national health system to ensure recognition of the chronic disease burden and sustain political commitment, allocate sufficient funding, and improve the delivery of chronic disease services at any health care level.

Different factors like the non-availability of drugs, poor diet adherence and lack of social support need to be taken into consideration in managing chronic health conditions in future pandemics because if immediate action is not taken, the economic impact of the disease could rise.

To prevent the psychological impact of the COVID-19, appropriate management should be urgently established by the government, health care personnel and other stakeholders. Developing advanced health care technologies that assist health care professionals is paramount to continue routine appointments.

Utilization rates of telehealth have increased during the pandemic period. This improved patient satisfaction, more effective routine disease monitoring, and increased treatment compliance. But still, the issue of knowledge, resources, internet accessibility, and ability in surfing the internet facility among resource-limited settings is challenging. Facilities and countries which have the facility and medical service processes should continue online, by phone, or by E-mail.

Current directions and perspectives

The coronavirus has clearly shown us how a “virus” negatively affected our lives in the 21 st century and simultaneously lead us to assure that the greatest assets of mankind are health, peace, love, solidarity, ingenuity, and knowledge [ 4 ].

People living with or affected by NCDs should continue to take their medication and follow medical advice, secure a one-month supply of your medication, or longer if possible [ 11 , 13 ]. All people living with the human immune virus (PLHIV) should take antiretroviral treatment (“treat all”) no more than seven days after confirmation of the diagnosis of HIV infection (“rapid initiation”), including same-day initiation if willing and eligible. Maintaining a good adherence to antiretroviral treatment (ART) can decrease viral suppression and increase immunity to reducing the risk of complications in case of infection with SARS-CoV-2 [ 56 , 57 ].

Patients having TB should follow the instructions given by their health care providers to combat the progress of their disease. Patients present with both TB and COVID-19 show similar symptoms such as cough, fever, and difficulty breathing. Also, both diseases attack primarily the respiratory system and they are transmitted mainly via close contact [ 40 , 41 ].

Some countries were providing services by the virtual-care framework using telehealth for more effective routine disease monitoring and improved patient satisfaction [ 17 ]. The community, health professionals, government, non-governmental organizations, and researchers should contribute to preventing the healthcare impact of COVID-19 on chronic disease patients.

Further, the use of apps can support the self-management of chronic conditions, like self-management of blood glucose and blood pressure. Finally, we recommend further investigation by voluntary researchers to carry out an extensive study to overcome the challenges and impacts of COVID 19 on chronic illness patients. Hopefully, this way, we can curtail and overcome the detrimental impact of delayed care on health outcomes for patients suffering from chronic diseases.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Disclosure of conflict of interest

Abbreviations.

Coronavirus disease 2019

Cardiovascular diseases

Human immune virus

Low- and middle-income countries

Noncommunicable chronic diseases

Non-communicable diseases

Tuberculosis

World Health Organization

• 1. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, Hu Y, Tao ZW, Tian JH, Pei YY, Yuan ML. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579:265–9. doi: 10.1038/s41586-020-2008-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 2. Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, Liu X, Wei L, Truelove SA, Zhang T, Gao W. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis. 2020;20:911–9. doi: 10.1016/S1473-3099(20)30287-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 3. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382:727–33. doi: 10.1056/NEJMoa2001017. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 4. Dubey S, Biswas P, Ghosh R, Chatterjee S, Dubey MJ, Chatterjee S, Lahiri D, Lavie CJ. Psychosocial impact of COVID-19. Diabetes Metab Syndr. 2020;14:779–88. doi: 10.1016/j.dsx.2020.05.035. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 5. Hajat C, Stein E. The global burden of multiple chronic conditions: a narrative review. Prev Med Rep. 2018;12:284–93. doi: 10.1016/j.pmedr.2018.10.008. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Cohen SP, Baber ZB, Buvanendran A, McLean BC, Chen Y, Hooten WM, Laker SR, Wasan AD, Kennedy DJ, Sandbrink F, King SA. Pain management best practices from multispecialty organizations during the COVID-19 pandemic and public health crises. Pain Med. 2020;21:1331–46. doi: 10.1093/pm/pnaa127. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 7. Xia Y, Li Q, Li W, Shen H. Elevated mortality of chronic diseases during COVID-19 pandemic: a cause for concern? Ther Adv Chronic Dis. 2020;11:1–3. doi: 10.1177/2040622320961590. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 8. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, Barnaby DP, Becker LB, Chelico JD, Cohen SL, Cookingham J. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. 2020;323:2052–9. doi: 10.1001/jama.2020.6775. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 9. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, Cereda D, Coluccello A, Foti G, Fumagalli R, Iotti G. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the lombardy region, Italy. JAMA. 2020;323:1574–81. doi: 10.1001/jama.2020.5394. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 10. Wolf MS, Serper M, Opsasnick L, O’Conor RM, Curtis L, Benavente JY, Wismer G, Batio S, Eifler M, Zheng P, Russell A. Awareness, attitudes, and actions related to COVID-19 among adults with chronic conditions at the onset of the US outbreak a cross-sectional survey. Ann Intern Med. 2020;173:100–109. doi: 10.7326/M20-1239. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 11. Kumar A, Nayar KR. COVID 19 and its mental health consequences. J Ment Health. 2021;30:1–2. doi: 10.1080/09638237.2020.1757052. [ DOI ] [ PubMed ] [ Google Scholar ]
• 12. Haybar H, Kazemnia K, Rahim F. Underlying chronic disease and COVID-19 infection: a state-of-the-art review. Jundishapur Journal of Chronic Disease Care. 2020;9:e103452. [ Google Scholar ]
• 13. Dyer O. Covid-19: pandemic is having “severe” impact on non-communicable disease care, WHO survey finds. BMJ. 2020;369:m2210. doi: 10.1136/bmj.m2210. [ DOI ] [ PubMed ] [ Google Scholar ]
• 14. Pal R, Bhadada SK. COVID-19 and non-communicable diseases. Postgrad Med J. 2020;96:429–30. doi: 10.1136/postgradmedj-2020-137742. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 15. Chudasama YV, Gillies CL, Zaccardi F, Coles B, Davies MJ, Seidu S, Khunti K. Impact of COVID-19 on routine care for chronic diseases: a global survey of views from healthcare professionals. Diabetes Metab Syndr. 2020;14:965–7. doi: 10.1016/j.dsx.2020.06.042. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 16. Danhieux K, Buffel V, Pairon A, Benkheil A, Remmen R, Wouters E, van Olmen J. The impact of COVID-19 on chronic care according to providers: a qualitative study among primary care practices in Belgium. BMC Fam Pract. 2020;21:1–6. doi: 10.1186/s12875-020-01326-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 17. Kendzerska T, Zhu DT, Gershon AS, Edwards JD, Peixoto C, Robillard R, Kendall CE. The effects of the health system response to the COVID-19 pandemic on chronic disease management: a narrative review. Risk Manag Healthc Policy. 2021;14:575–84. doi: 10.2147/RMHP.S293471. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 18. Williams S, Tsiligianni I. COVID-19 poses novel challenges for global primary care. NPJ Prim Care Respir Med. 2020;30:30. doi: 10.1038/s41533-020-0187-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 19. Kouri A, Gupta S, Yadollahi A, Ryan CM, Gershon AS, To T, Tarlo SM, Goldstein RS, Chapman KR, Chow CW. CHEST Reviews: Addressing reduced laboratory-based pulmonary function testing during a pandemic. Chest. 2020;158:2502–2510. doi: 10.1016/j.chest.2020.06.065. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 20. Steve G. Chronic, noncommunicable diseases (NCDs): a silent scourge threatening to overwhelm global health. Advancing Science for Global Health. 2011;10:7–13. [ Google Scholar ]
• 21. Ekpenyong CE, Udokang NE, Akpan EE, Samson TK. Double burden, non-communicable diseases and risk factors evaluation in Sub-Saharan Africa: the Nigerian experience. European Journal of Sustainable Development. 2012;1:249–70. [ Google Scholar ]
• 22. Tirschwell DL, Ton TG, Ly KA, Van Ngo Q, Vo TT, Pham CH, Longstreth WT, Fitzpatrick AL. A prospective cohort study of stroke characteristics, care, and mortality in a hospital stroke registry in Vietnam. BMC Neurol. 2012;12:150. doi: 10.1186/1471-2377-12-150. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 23. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, De Ferranti S, Després JP, Fullerton HJ, Howard VJ. Heart disease and stroke statistics-2016 update: a report from the american heart association. Circulation. 2016;133:e38–e360. doi: 10.1161/CIR.0000000000000350. [ DOI ] [ PubMed ] [ Google Scholar ]
• 24. Song J, Zeng M, Wang H, Qin C, Hou HY, Sun ZY, Xu SP, Wang GP, Guo CL, Deng YK, Wang ZC. Distinct effects of asthma and COPD comorbidity on disease expression and outcome in patients with COVID-19. Allergy. 2021;76:483–96. doi: 10.1111/all.14517. [ DOI ] [ PubMed ] [ Google Scholar ]
• 25. Chen Y, Gong X, Wang L, Guo J. Effects of hypertension, diabetes and coronary heart disease on COVID-19 diseases severity: a systematic review and meta-analysis. MedRxiv. 2020 [ Google Scholar ]
• 26. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, Ji R, Wang H, Wang Y, Zhou Y. Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. Int J Infect Dis. 2020;10:91–95. doi: 10.1016/j.ijid.2020.03.017. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 27. Peng YD, Meng K, Guan HQ, Leng L, Zhu RR, Wang BY, He MA, Cheng LX, Huang K, Zeng QT. Clinical characteristics and outcomes of 112 cardiovascular disease patients infected by 2019-nCoV. Zhonghua Xin Xue Guan Bing Za Zhi. 2020;48:450–5. doi: 10.3760/cma.j.cn112148-20200220-00105. [ DOI ] [ PubMed ] [ Google Scholar ]
• 28. McDonald HI, Thomas SL, Nitsch D. Chronic kidney disease as a risk factor for acute community-acquired infections in high-income countries: a systematic review. BMJ Open. 2014;4:e004100. doi: 10.1136/bmjopen-2013-004100. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 29. Hall V, Thomsen RW, Henriksen O, Lohse N. Diabetes in Sub Saharan Africa 1999-2011: epidemiology and public health implications. A systematic review. BMC Public Health. 2011;11:564. doi: 10.1186/1471-2458-11-564. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 30. Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN. Cardiovascular disease, drug therapy, and mortality in COVID-19. N Engl J Med. 2020;382:e102. doi: 10.1056/NEJMoa2007621. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [ Retracted ]
• 31. The Lancet Respiratory Medicine. COVID-19 heralds a new era for chronic diseases in primary care. Lancet Respir Med. 2020;8:647. doi: 10.1016/S2213-2600(20)30274-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 32. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, Ho RC. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health. 2020;17:1729. doi: 10.3390/ijerph17051729. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 33. Duan L, Zhu G. Psychological interventions for people affected by the COVID-19 epidemic. Lancet Psychiatry. 2020;7:300–2. doi: 10.1016/S2215-0366(20)30073-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 34. Action H. Surviving in place: the coronavirus domestic violence syndemic. Asian J Psychiatr. 2020;53:102179. doi: 10.1016/j.ajp.2020.102179. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 35. Stankovska G, Memedi I, Dimitrovski D. Coronavirus COVID-19 disease, mental health and psychosocial support. Society Register. 2020;4:33–48. [ Google Scholar ]
• 36. Ozamiz-Etxebarria N, Dosil-Santamaria M, Picaza-Gorrochategui M, Idoiaga-Mondragon N. Stress, anxiety, and depression levels in the initial stage of the COVID-19 outbreak in a population sample in the northern Spain. Cad Saude Publica. 2020;36:e00054020. doi: 10.1590/0102-311X00054020. [ DOI ] [ PubMed ] [ Google Scholar ]
• 37. Moukaddam N, Shah A. Psychiatrists Beware! The impact of COVID-19 and pandemics on mental health. Psychiatric Times. 2020;37:11–2. [ Google Scholar ]
• 38. Jaiswal J, Halkitis PN. Towards a more inclusive and dynamic understanding of medical mistrust informed by science. Behav Med. 2019;45:79–85. doi: 10.1080/08964289.2019.1619511. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 39. Yao H, Chen JH, Xu YF. Patients with mental health disorders in the COVID-19 epidemic. Lancet Psychiatry. 2020;7:e21. doi: 10.1016/S2215-0366(20)30090-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 40. McQuaid CF, McCreesh N, Read JM, Sumner T, Houben RM, White RG, Harris RC. The potential impact of COVID-19-related disruption on tuberculosis burden. Eur Respir J. 2020;56:2001718. doi: 10.1183/13993003.01718-2020. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 41. Hogan AB, Jewell BL, Sherrard-Smith E, Vesga JF, Watson OJ, Whittaker C, Hamlet A, Smith JA, Winskill P, Verity R, Baguelin M. Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: a modelling study. Lancet Glob Health. 2020;8:e1132–e41. doi: 10.1016/S2214-109X(20)30288-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 42. Addis SG, Nega AD, Miretu DG. Psychological impact of COVID-19 pandemic on chronic disease patients in Dessie town government and private hospitals, Northeast Ethiopia. Diabetes Metab Syndr. 2021;15:129–35. doi: 10.1016/j.dsx.2020.12.019. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 43. Kretchy IA, Asiedu-Danso M, Kretchy JP. Medication management and adherence during the COVID-19 pandemic: perspectives and experiences from LMICs. Res Social Adm Pharm. 2021;17:2023–2026. doi: 10.1016/j.sapharm.2020.04.007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 44. Shigemura J, Ursano RJ, Morganstein JC, Kurosawa M, Benedek DM. Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan: mental health consequences and target populations. Psychiatry Clin Neurosci. 2020;74:281–2. doi: 10.1111/pcn.12988. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 45. Agampodi TC, Agampodi SB, Glozier N, Siribaddana S. Measurement of social capital in relation to health in low and middle income countries (LMIC): a systematic review. Soc Sci Med. 2015;128:95–104. doi: 10.1016/j.socscimed.2015.01.005. [ DOI ] [ PubMed ] [ Google Scholar ]
• 46. Shiferaw F, Letebo M, Misganaw A, Feleke Y, Gelibo T, Getachew T, Defar A, Assefa A, Bekele A, Amenu K, Teklie H. Non-communicable Diseases in Ethiopia: disease burden, gaps in health care delivery and strategic directions. Ethiopian Journal of Health Development. 2018;32:170–80. [ Google Scholar ]
• 47. McGregor S, Henderson KJ, Kaldor JM. How are health research priorities set in low and middle income countries? A systematic review of published reports. PLoS One. 2014;9:e108787. doi: 10.1371/journal.pone.0108787. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 48. Yancy CW. COVID-19 and African Americans. JAMA. 2020;323:1891–2. doi: 10.1001/jama.2020.6548. [ DOI ] [ PubMed ] [ Google Scholar ]
• 49. Massinga Loembé M, Tshangela A, Salyer SJ, Varma JK, Ouma AEO, Nkengasong JN. COVID-19 in Africa: the spread and response. Nat Med. 2020;26:999–1003. doi: 10.1038/s41591-020-0961-x. [ DOI ] [ PubMed ] [ Google Scholar ]
• 50. Sørensen JM. Herb-drug, food-drug, nutrient-drug, and drug-drug interactions: mechanisms involved and their medical implications. J Altern Complement Med. 2002;8:293–308. doi: 10.1089/10755530260127989. [ DOI ] [ PubMed ] [ Google Scholar ]
• 51. Caldeira D, Vaz-Carneiro A, Costa J. The impact of dosing frequency on medication adherence in chronic cardiovascular disease: systematic review and meta-analysis. Rev Port Cardiol. 2014;33:431–7. doi: 10.1016/j.repc.2014.01.013. [ DOI ] [ PubMed ] [ Google Scholar ]
• 52. Watkins P, Alemu S. Delivery of diabetes care in rural Ethiopia: an experience from Gondar. Ethiop Med J. 2003;41:9–17. [ PubMed ] [ Google Scholar ]
• 53. Prevett M. Chronic non-communicable diseases in ethiopia-a hidden burden. Ethiop J Health Sci. 2012;22:1–2. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 54. Woldesemayat EM, Kassa A, Gari T, Dangisso MH. Chronic diseases multi-morbidity among adult patients at Hawassa university comprehensive specialized hospital. BMC Public Health. 2018;18:352. doi: 10.1186/s12889-018-5264-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 55. Verma A, Rajput R, Verma S, Balania VKB, Jangra B. Impact of lockdown in COVID 19 on glycemic control in patients with type 1 diabetes mellitus. Diabetes Metab Syndr. 2020;14:1213–6. doi: 10.1016/j.dsx.2020.07.016. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 56. Jiang H, Zhou Y, Tang W. Maintaining HIV care during the COVID-19 pandemic. Lancet HIV. 2020;7:e308–e9. doi: 10.1016/S2352-3018(20)30105-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 57. Blanco JL, Ambrosioni J, Garcia F, Martínez E, Soriano A, Mallolas J, Miro JM COVID-19 in HIV Investigators. COVID-19 in patients with HIV: clinical case series. Lancet HIV. 2020;7:e314–e316. doi: 10.1016/S2352-3018(20)30111-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
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A literature review of the economics of COVID‐19

Abel brodeur, suraiya bhuiyan.

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Correspondence , Abel Brodeur, Department of Economics, University of Ottawa, 120 University Private, 9th floor, Ottawa, ON K1N 6N5 Canada. Email: [email protected]

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Issue date 2021 Sep.

This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

The goal of this piece is to survey the developing and rapidly growing literature on the economic consequences of COVID‐19 and the governmental responses, and to synthetize the insights emerging from a very large number of studies. This survey: (i) provides an overview of the data sets and the techniques employed to measure social distancing and COVID‐19 cases and deaths; (ii) reviews the literature on the determinants of compliance with and the effectiveness of social distancing; (iii) mentions the macroeconomic and financial impacts including the modelling of plausible mechanisms; (iv) summarizes the literature on the socioeconomic consequences of COVID‐19, focusing on those aspects related to labor, health, gender, discrimination, and the environment; and (v) summarizes the literature on public policy responses.

Keywords: COVID‐19, coronavirus, economic impact, lockdowns, social impact

1. INTRODUCTION

The World was gripped by a pandemic over the first half of 2020, of which the second wave emerged in the Fall. It was identified as a new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS‐CoV‐2), and later renamed as Coronavirus Disease‐19 or COVID‐19 (Qiu et al., 2020 ). While COVID‐19 originated in the city of Wuhan in the Hubei province of China, it has spread rapidly across the World, resulting in a human tragedy and in tremendous economic damage. By the end of November 2020, there had been close to 63 million reported cases of COVID‐19 globally and over 1.4 million deaths.

Pandemics are anything but new, and they have had severe, adverse economic impacts in the past; COVID‐19 is not expected to be any different (see the Online Appendix for a brief history of past pandemics and their socioeconomic consequences). Given the rapid spread of COVID‐19, countries across the World have adopted several public health measures intended to prevent its spread, including social distancing (Fong et al., 2020 ). According to Mandavilli ( 2020 ), this strategy saved thousands of lives, both during other pandemics, such as the Spanish flu of 1918, and more recently a flu outbreak that occurred in Mexico City in 2009. As part of social distancing measures, businesses, schools, community centers, and nongovernmental organization (NGOs) were required to close down, mass gatherings have been prohibited, and lockdown measures have been imposed in many countries, allowing travel only for essential needs. 1 The goal of these measures is to facilitate a “flattening the curve,” that is, a reduction in the number of new daily cases of COVID‐19 in order to halt their exponential growth and, hence, reduce pressure on medical services (John Hopkins University, 2020 ).

The spread of COVID‐19 has resulted in a considerable slowdown in economic activities. According to an early forecast of The World Bank ( 2020 ), global GDP in 2020 relative to 2019 is forecasted to fall by 5.2%. Similarly, the OECD ( 2020 ) forecasts a fall in global GDP by 6 to 7.6%, depending on whether or not a second wave of COVID‐19 emerges. In its latest forecast, the International Monetary Fund ( 2020 ) projected a contraction of 4.4% in light of the stronger than expected recoveries in advanced economies which lifted lockdowns during May and June of 2020. This was mainly the result of the unprecedented fiscal, monetary, and regulatory responses in these countries that helped to maintain household disposable income, protect cash flows for firms, and support credit provisions.

The economic implications will be wide ranging and uncertain, with different effects expected on labor markets, production supply chains, financial markets, and GDP levels. The negative effects may vary by the stringency of the social distancing measures (e.g., lockdowns and related restrictions), their length of implementation, and the degree of compliance with them. In addition, the pandemic and the subsequent interventions may well lead to higher levels of mental health distress, increased economic inequality, and particularly harsh effects on certain socio‐demographic groups.

The goal of this piece is to survey the emerging and already vast literature on the economic consequences of COVID‐19, and to synthesize the insights contained in a growing number of studies. Figure  1 illustrates the number of National Bureau of Economic Research (NBER) working articles that have been released related to the pandemic between March and November of 2020. 2 By the end of November 2020, there had been 247 articles related to COVID‐19. Similarly, 204 discussion articles on the pandemic were released by the IZA Institute of Labor Economics (IZA) from March to November of 2020. 3

COVID‐19 publications in 2020 in the NBER working paper series. [Color figure can be viewed at wileyonlinelibrary.com ]

Source : Authors’ compilation drawn from the NBER website

This article will focus on five broad areas: (i) the measurement of the spread of COVID‐19 and social distancing activities, (ii) the effectiveness and compliance with social distancing regulations, (iii) the economic impacts of COVID‐19 and the mechanisms giving rise to them, (iv) the socioeconomic consequences of lockdowns, and (v) the policy measures and regulations that have been implemented in response to the pandemic. One topic that we do not cover explicitly is the interface between COVID‐19 and financial markets. This omission is due partly to space constraints, but also to the fact that the outcomes in financial markets that are related to COVID‐19 are extremely volatile, and therefore, any analysis contained in our survey would be ephemeral.

The rest of the article is structured as follows. Section  2 provides an outline of the measurement of COVID‐19 spread and of social distancing actions by documenting and describing the most popular data sources. Section  3 discusses the socioeconomic determinants and the effectiveness of social distancing activities. Section  4 focuses on the economic and financial impacts including modelling of the plausible behavioral mechanisms. Section  5 reviews the literature on the socioeconomic consequences of social distancing measures, focusing on the labor‐related, health‐related, gender‐related, discriminatory, and environmental aspects. Section  6 consists of a summary of the economic impact of the policy responses. Section  7 provides the conclusion.

2. MEASUREMENT OF COVID‐19 AND SOCIAL DISTANCING ACTIONS

2.1. measurement of covid‐19 spread.

Before reviewing the potential economic impact and socioeconomic consequences, it is important to contextualize the data related to COVID‐19, without which it would not be possible to assess the scope of the pandemic. Timely and reliable data inform us of how and where the disease is spreading, what impact the pandemic has on the lives of people around the World, and to what extent the counter measures that are taken are successful (Roser et al., 2020 ).

Four key indicators are: (i) the total number of tests carried out, (ii) the number of confirmed COVID‐19 cases, (iii) the number of confirmed COVID‐19 deaths, and (iv) the number of people who have recovered from COVID‐19. These numbers are provided by different local, regional, and national health agencies/ministries across countries. However, for research and educational purposes, the data are accumulated by the Center for Systems Science and Engineering at Johns Hopkins University. 4 The database provides the figures as well as visual maps of the distribution of cases across the World. They are reported at the provincial level for China, at the city level for the United States of America, Australia, and Canada, and at the country level for all other countries (Dong et al., 2020 ). The data are corroborated with the WHO, 5 the Center for Disease Control (CDC) in the United States, and the European Center for Disease Control (ECDC).

Based on these figures, the Case Fatality Rate (CFR) is calculated as the number of confirmed deaths divided by the number of confirmed cases, which gives the mortality rate. 6 However, Roser et al. ( 2020 ) caution against taking the CFR numbers at face value to assess mortality risks, 7 because the CFR is based on the number of confirmed cases. Due to limited and sporadic testing capacities, not all COVID‐19 cases can be confirmed. Moreover, the CFR reflects the incidence of the disease in a particular context at a particular point in time. Therefore, CFRs are subject to changes over time and are sensitive to the location and population characteristics.

Recent studies indicate that there are large measurement errors associated with COVID‐19 case numbers. Using data on influenza‐like illnesses (ILI) from the CDC, Silverman et al. ( 2020 ) show that ILIs can be a useful predictor of COVID‐19 cases in the United States. The authors find that there was an escalation in the number of ILI patients during March of 2020. These cases could not be properly identified as COVID‐19 cases due to the lack of testing capabilities during the early stages of the pandemic's progression. The authors suggest that the surge in ILIs may have corresponded to 8.7 million new COVID‐19 cases between March 8 and March 28, most of which were probably not diagnosed. Based on imputation, that figure suggests that almost 80% of all actual cases in the United States during that time period were never diagnosed.

While the dataset mentioned above focuses on counts and tests, the COVID Tracking Project 8 in the United States provides additional data on patients who have been hospitalized, are in intensive care units (ICUs), and are on ventilator support for each of the 50 states. It also grades each state on data quality. Recently, it has included the COVID Racial Data Tracker , 9 which shows the race and the ethnicity of individuals affected by COVID‐19. All of these combined measures and statistics provide a more comprehensive perspective of the spread of the pandemic in the United States.

2.2. Measurement of social distancing

Compared to measuring the spread of the virus, social distancing is not easy to quantify. We determined from the literature that there are three main techniques that are employed: (i) developing and calculating measures of the mobility of the population, (ii) modelling proxies, and (iii) calculating indices. Proxies and indices are based on data related to the observed spread of infection and to the implementation of social distancing policies, respectively. On the other hand, the movements of people are based on their observed travelling patterns. Mobility measures have been used extensively in recent months to discern mobility patterns during the pandemic (Nguyen et al., 2020 ). However, mobility data providers have slight differences in their methodologies. Table  1 provides a summary of how different mobility data providers compile their data.

Social distancing— Mobility measures and how they work

Mobility data are more dynamic and are available at a daily frequency. They can also be used to measure the effect of social distancing on other variables, such as adherence to shelter‐in‐place policies or labor employment patterns (Gupta et al., 2020 ). They also offer key insights into human behavior. For example, “Safegraph” data suggest that social activity in the United States started declining substantially and rapidly well before lockdown measures were imposed (Farboodi et al., 2020 ).

Outside of the United States, a large number of studies have relied on Google LLC Community Mobility Reports. For China, mobility has been mostly measured using data from Baidu Inc. For example, Kraemer et al. ( 2020 ) document how COVID‐19 spread in China using Baidu Inc. data. They investigate travel history from Wuhan to other cities in China, finding that the spatial distribution of cases in other cities was correlated with individual peoples’ travel histories. However, after the implementation of social distancing measures in these cities, the correlation no longer held. Therefore, the authors conclude that local lockdowns rather than travel restrictions helped to mitigate the spread and transmission of COVID‐19 in cities outside Wuhan. See Coelho et al. ( 2020 ) for an examination of the spread of COVID‐19 in Brazil using daily air travel statistics from the Official Airline Guide to measure mobility.

Mobility data do have their own limitations and are not frequently used in the case of epidemics, even though they might be useful (Oliver et al., 2020 ). Mobility data are a proxy for time spent in different locations. They do not allow one to determine the situational context of the contacts that are reported, which are needed to understand the spread of COVID‐19, that is whether they occur in the workplace or in the general community (Martín‐Calvo et al., 2020 ). Those two situations involve different levels of the inherent risk of transmission. In regards to the productive activities of the individuals that are tracked, information on the context is also indeterminate. For those who are working virtually from their homes, for instance, these measures do not capture the value‐added stemming from the time that they allocate to their jobs in the labor market. It is also likely that the quality of these measures can deteriorate when overall unemployment rates and job disruptions are high (Gupta et al., 2020 ). 10 Telecom operator data are deemed to be more representative than locational data, as the former are not limited to people with smartphones, GPS locators, and histories of travel using GPS location (Lomas, 2020 ).

Social media has also been used to measure mobility patterns. Galeazzi et al. (2020) analyze the effect of lockdowns in France, Italy, and United Kingdom on national mobility patterns by exploiting geolocalized data observed from 13 million Facebook users. The authors predictably find that people transition toward localized, short‐ range mobility patterns instead of international, long‐range patterns. However, mobility patterns display heterogeneity across countries. In France and the United Kingdom, mobility is more “concentrated” around huge, central metropolises that are largely disconnected from the provinces, which helps to reduce transmission of the virus. In Italy, on the other hand, the population is more “distributed” across clusters around four major cities that remain interconnected, thus permitting persistent spread.

3. SOCIAL DISTANCING: DETERMINANTS, EFFECTIVENESS, AND COMPLIANCE

A large range of social distancing policies have been implemented, ranging from full‐scale lockdowns to voluntary self‐compliance measures. 11 For example, Sweden imposed relatively light restrictions (Juranek & Zoutman, 2020 ). Large‐scale events were prohibited, and restaurants and bars were restricted to table service only; however, private businesses were generally allowed to operate freely. The population was encouraged to stay at home if they were feeling unwell and to limit social interactions if possible (T.M. Andersen et al., 2020 bib18 ).

People tend to adopt social distancing practices when there is a specific incentive to do so in terms of risk to health and financial cost (Makris, 2020 ). Maloney and Taskin ( 2020 ) attribute voluntary, cooperative actions to either fear of infection or to a sense of social responsibility. Stringent social distancing measures tend to be implemented in countries with a greater proportion of elderly residents, a higher population density, a greater proportion of employees working in vulnerable occupations, higher degrees of democratic freedom, a higher incidence of international travel, and greater distances from the Equator (e.g., Jinjarak et al., 2020 ). Appealing to a game theoretic approach, Cui et al. ( 2020 ) argue that states sharing economic ties will be “tipped” to reach a Nash equilibrium, whereby all other states comply with shelter‐in‐place policies. 12

Social distancing policy determinants have been linked to political party characteristics, political beliefs, and partisan differences (Baccini & Brodeur, 2021 ; Barrios & Hochberg, forthcoming; Murray & Murray, 2020 ). Barrios and Hochberg (forthcoming) correlate the risk perception for contracting COVID‐19 with partisan differences. They find that, in the absence of the imposition of social distancing, counties in the United States which had higher vote shares for Donald Trump are less likely to engage in social distancing. This persists even when mandatory stay‐at‐home measures are implemented across states. Allcott et al. ( 2020 ) find a similar pattern. In addition, the authors show through surveys that Democratic and Republican supporters have different risk perceptions about contracting COVID‐19, and hence hold divergent views regarding the importance of following social distancing measures. These stylized facts make it hard to estimate the causal effect of COVID‐19 on electoral outcomes (Baccini et al., 2021 ).

Researchers are trying to determine the effectiveness of social distancing policies in reducing social interactions and ultimately infections and deaths. Abouk and Heydari ( 2021 ) show that reductions in outside‐the‐home social interactions in the United States are driven by a combination of governmental regulations and voluntary measures, with a strong causal impact for the implementation of statewide stay‐at‐home orders, but more moderate impacts for nonessential business closures and limitations placed on bars/restaurants. Ferguson et al. ( 2020 ) argue that multiple interventions are required in order to have a substantial desired impact on transmission. The optimal mitigation strategy, which is a combination of case isolations, home quarantining, and social distancing of high‐risk groups, would reduce the number of deaths by half and the demand for beds in ICUs by two‐thirds in the United Kingdom and the United States.

Some studies focus on the impact of social distancing on COVID‐19 cases, hospitalizations, etc. For example, Fang et al. ( 2020 ) argue that if lockdown policies had not been imposed in Wuhan, then the infection rates would have been 65% higher in cities outside of Wuhan. Hartl et al. ( 2020 ) show that growth rate of COVID‐19 cases in Germany dropped from 26.7 to 13.8% within 7 days after implementation of lockdowns in the country. Greenstone and Nigam ( 2020 ) project that 3 to 4 months of adherence to social distancing regulations would reduce the number of cases in the United States by 1.7 million by October of 2021, 630,000 of which would translate into averted overcrowding of ICUs in hospitals. Friedson et al. ( 2020 ) argue that early intervention in California helped to reduce significantly the numbers of COVID‐19 cases and deaths during the first 3 weeks following its enactment. Note that this set of interventions falls well short of an economic shutdown.

Similarly, Dave, Friedson, Matsuzawa, Sabia, et al. ( 2020 ) find that counties in Texas that adopted shelter‐in‐place orders earlier than the statewide shelter‐in‐place order experienced a 19 to 26% fall in the rates of COVID‐19 case growth 2 weeks after implementation of such orders. M. Andersen et al. ( 2020 ) find that temporary paid sick leave, a federal mandate enacted in the United States, which allowed private and public employees 2 weeks of paid leave, led to increased compliance with stay‐at‐home orders. On a more global scale, Hsiang et al. ( 2020 ) show that social distancing interventions prevented or delayed around 62 million confirmed cases, corresponding to the aversion of roughly 530 million total infections in China, South Korea, Italy, Iran, France, and the United States within 7 days.

Another important related issue is the determinants of compliance behavior (e.g., Coelho et al., 2020 ; Fan et al., 2020 ). The documented socioeconomic determinants of the degree of compliance with social distancing (lockdowns or safer‐at‐home orders) include, among other factors, income level, trust, and social capital, public discourse, and to some extent, news channel viewership. The degree of ethnic diversity is another documented socioeconomic determinant of social distancing (Egorov et al., 2021 ). Galasso et al. ( 2020 ) rely on survey data from eight OECD countries and provide evidence that women are more likely than men to agree with restrictive public policy measures and to comply with them. Chiou and Tucker ( 2020 ) show that Americans living in higher‐income regions with access to high‐speed internet are more likely to comply with social distancing directives. Coven and Gupta ( 2020 ) find that residents of low‐income neighborhoods in New York City comply less with shelter‐in‐place activities during non‐working hours. According to the authors, this pattern is consistent with the fact that low‐income populations are more likely to be front line, “essential” workers and are also are more likely to make frequent retail shopping visits for essentials, making for two compounded effects. People with lower income levels, less flexible work arrangements (e.g., the inability to work remotely), and a lack of accessible interior space outside of bedrooms are less likely to engage in social distancing (Papageorge et al., 2020 ). Last, Bonaccorsi et al. ( 2020 ) analyze the heterogeneous impacts of lockdowns by socioeconomic conditions of people in Italy. Using mobility data from Facebook, they provide evidence that mobility reduction is higher in municipalities which have stronger fiscal capacity and also those which have lower per‐capita income levels. The authors conclude that the pandemic has disproportionately affected poor individuals within municipalities with strong fiscal capacity in Italy.

Individual beliefs and social preferences should also be taken into consideration, as they affect behavior and compliance. Based on an experimental setup with participants in the United States and the United Kingdom, Akesson et al. ( 2020 ) conclude that individuals overestimated the infectiousness of COVID‐19 relative to expert suggestions. If they were exposed to expert opinion, individuals were prone to correct their beliefs. However, the more infectious COVID‐19 was deemed to be, the less likely they were to undertake social distancing measures. This was perhaps due to the belief that the individual will contract COVID‐19 regardless of his/her social distancing practices. Briscese et al. ( 2020 ) model the impact of “lockdown extension” on compliance using a representative sample of residents from Italy. The authors find that if a given hypothetical extension is shorter than expected (i.e., a positive surprise), the residents are more willing to engage in self‐isolation. Therefore, to ensure compliance, these authors suggest that it is imperative for the government or local authorities to work on communication and to manage peoples’ expectations. Campos‐Mercade et al. ( 2021 ) examine the relationship between social preferences and social distancing compliance. The authors find that people who exhibit prosocial behavior (in this instance individuals who claim that they do not want to expose others to risks) are more likely to follow social distancing measures and other health‐related guidelines.

Bargain and Aminjonov ( 2020 ) demonstrate that residents in European regions with high levels of trust decrease their mobility related to non‐necessary activities compared to regions with lower levels of trust. Brück et al. ( 2020 ) document a negative relationship between being in contact with sick people and trust in people and institutions. Similarly, Brodeur et al. ( 2020 ) find that counties in the United States exhibiting relatively more trust in others decrease their mobility significantly once a lockdown policy is implemented. They also provide evidence that the estimated effect on postlockdown compliance is especially large if people tend to place trust in the media, and relatively smaller if they tend to trust in science, medicine, or government.

Researchers also think about this chain of causality in reverse. Aksoy Eichengreen, and Saka ( 2020 ) find that individuals’ degrees of exposure to epidemics (especially during the ages 18 to 25) has a negative effect on their confidence in political institutions. These individuals are also less likely to have confidence in their health care systems during the times of pandemics. Barrios et al. ( 2021 ) and Durante et al. ( 2021 ) provide evidence that regions with stronger civic culture engaged in more voluntary social distancing. Aksoy, Ganslmeier, and Poutvaara ( 2020 ) find that a high level of public attention (measured through the share of Google shares containing matters related to COVID‐19) has a significant correlation with the timing of implementation of social distancing measures. This relationship is mostly applicable for countries with high quality of institutions. Last, Bartscher et al. ( 2020 ) show that higher levels of social capital (proxied through voter turnout in parliamentary elections) lead to fewer cases per capita accumulated from mid‐March to mid‐May in selected European countries and United Kingdom.

Daniele et al. ( 2020 ) investigate the effect of the COVID‐19 shock on sociopolitical attitudes as opposed to the impact of latter on the spread of the virus. Employing a randomized survey flow design for 8,000 respondents in Germany, Italy, Netherlands and Spain, the authors find that COVID‐19 has led to a deterioration in the levels of interpersonal and institutional trust. It has also lowered support for the European Union in general and for social welfare spending financed by taxes. The authors conclude that these results are driven by the “economic insecurity” rather than the “health” dimensions resulting from the crisis.

Simonov et al. ( 2020 ) analyze the causal effect of cable news viewership on social distancing compliance. The authors examine the average partial effect of Fox News viewership, a news channel that has mostly refuted expert recommendations from leaders of the United States and global public health communities on the severity of COVID‐19 and on compliance, and find that a 1 percentage point increase in Fox News viewership reduced the propensity to stay at home by 8.9 percentage points. Bursztyn et al. ( 2020 ) show that greater exposure to the Hannity show compared to the Tucker Carlson Tonight show in Fox News is associated with larger COVID‐19 case numbers and deaths. This is because the former TV host downplayed the importance of COVID‐19, while the latter provided a serious warning on the same topic during early February. The variation between the messages in the two shows led to changes in behavior in response to COVID‐19.

Table  2 provides a summary of the literature related to the determinants (i.e., factors which influence implementation of social distancing as a policy measure), compliance with social distancing (i.e., whether people are actually following social distancing measures), and their effectiveness (i.e., evidence of success in reducing COVID‐19 cases).

Determinants, compliance and effectiveness of social distancing measures: Summary of studies

4. MACROECONOMIC IMPACTS AND PLAUSIBLE MECHANISMS

4.1. plausible mechanisms for macroeconomic impact.

To understand the potential negative economic impact of COVID‐19, it is important to comprehend the economic transmission channels through which the shocks will adversely affect the economy. According to Carlsson‐Szlezak et al. ( 2020a , b ), there are three main transmission channels. The first is the direct impact, which is related to reduced consumption of goods and services. Prolonged lengths of the pandemic and the concomitant social distancing measures might reduce consumer confidence by keeping consumers at home, wary of discretionary spending, and pessimistic about their long‐term economic prospects. The second one is the indirect impact working through financial market shocks and their effects on the real economy. Household wealth will likely fall, savings will increase, and consumption spending will decrease further. The third consists of supply‐side disruptions; as restrictions halt or hamper production activities, they will negatively impact supply chains, labor demand, and employment, leading to prolonged periods of lay‐offs and rising unemployment. In particular, Baldwin ( 2020 ) discusses the expectation shock by which a “wait‐and‐see” attitude is adopted by economic agents. The author argues that this is common during economic climates characterized by uncertainties, as there is less confidence in markets and in engaging in economic transactions. Ultimately, the intensity of the shock is determined by the underlying epidemiological properties of the virus, consumer behaviour, and firm behavior in the face of adversity and uncertainty, and public policy responses. To understand the implications of the spread of the virus and the consequent social distancing measures on economic activities, a number of researchers have integrated canonical epidemiology models such as the susceptible, infected, resolved model (SIR) with macroeconomic models (see the Online Appendix for a detailed review of these models).

Gourinchas ( 2020 , p. 33) summarizes the effect on the economy by stating: “A modern economy is a complex web of interconnected parties: employees, firms, suppliers, consumers, and financial intermediaries. Everyone is someone else's employee, customer, lender, etc.” Due to the very high degrees of interconnectiveness and specialization of productive activities, a breakdown in the supply chains and the circular flows will have cascading effects. Baldwin ( 2020 ) describes the impact of COVID‐19 and subsequent social distancing measures on the macroeconomy within a circular flow framework.

It is also important to understand the processes that generate recoveries from economic crises. Carlsson‐Szlezak et al. ( 2020a ) explain different types of recovery in the aftermath of negative shocks through the concept of “shock geometry.” There are three broad scenarios of economic recoveries, which we mention in ascending order of their severity. First, there is the most optimistic one labelled “V‐shaped,” whereby aggregate output is displaced and quickly recovers to its pre‐crisis path. Second, there is the “U‐shaped” path, whereby output drops swiftly but does not return swiftly to its precrisis path. The gap between the former trajectory of output and the actual one remains large for quite some time, but recovery eventually occurs. Third, in the case of the very grim “L‐shaped” path, output drops and reaches a trough, but subsequent growth rates remain very low. The gap between the former and the new output paths continues to widen. Another scenario of economic recovery often mentioned is the “K‐shaped” one, which occurs when, following a recession, different parts of the economy recover at different rates, times, or magnitudes.

Carlsson‐Szlezak et al. ( 2020b ) state that after previous pandemics, such as the 1918 Spanish Influenza, the 1958 Asian Influenza, the 1968 Hong Kong influenza, and the 2002 SARS outbreak, economies have tended to experience “V‐shaped” recoveries. However, the pattern for the COVID‐19 economic recovery is not expected to be straightforward. This is because the effects on employment due to social distancing measures and lockdowns are expected to be much larger. According to Gourinchas ( 2020 ), during a short period, as much as 50% of the working population might not be able to find work. Moreover, even if no containment measures are implemented, a recession would occur anyway, fueled by the precautionary and/or risk‐averse behavior of households and firms faced with the uncertainty of dealing with a pandemic as well as with an inadequate public health response (Gourinchas, 2020 ).

Guerrieri et al. ( 2020 ) show that in a multisector model with certain assumptions, such as incomplete markets, low substitutability across sectors, and liquidity‐constrained consumers, COVID‐19 imparts a supply shock which works through lockdowns, layoffs, firm closures, etc. The subsequent impact would be a drop in aggregate demand and a demand‐deficient recession, that is, a “Keynesian supply shock.”

Baqaee and Farhi ( 2020 ) analyze the impact through a disaggregated Keynesian model comprised of multiple sectors, factors of production, and input‐output linkages with different features, such as nominal wage rigidities and credit constraints. They find that negative supply shocks are stagflationary, whereas negative demand shocks are deflationary. The policy implications are somewhat ambiguous. Policies that boost aggregate supply (e.g., providing subsidies to businesses, relaxing lockdowns, etc.) might not be effective in increasing demand in certain demand‐constrained sectors. Similarly, demand‐inducing policies (e.g., lower interest rates, more generous social insurance, etc.) might lead to supply shortages and inflationary pressures in certain sectors.

4.2. Quantitative macroeconomic impacts

As the pandemic unfolds, many researchers have been thinking about the economic impact from a historical perspective. Ludvigson et al. ( 2020 ) try to quantify the macroeconomic impact of costly disasters (natural and manmade) and translate them into estimates of the impact of COVID‐19. They find that in a fairly conservative scenario, pandemics, such as COVID‐19, are tantamount to large, multiple‐period exogenous shocks. Using a “costly disaster” index, the authors find that COVID‐19 is constituted of multi‐period shocks in the United States, which leads to a 12.75% drop in industrial production, a 17% loss in service employment, sustained and drastic reductions in air travel, and macroeconomic uncertainties which linger for up to 5 months. Jordà et al. ( 2020 ) analyze the rate of return on the real natural interest rate (the level of real returns on safe assets resulting from the demand and supply of investment capital in a noninflationary environment) from the 14 th century to 2018. Theoretically, a pandemic is supposed to induce a downward negative shock to the real natural interest rate. This is because investment demand decreases due to excess capital per labor unit (i.e., a scarcity of labor being utilized), while savings flows increase due to either precautionary reasons or to replace lost wealth. The authors find that the natural rate of interest may be about 2 percentage points lower than it would otherwise have been some 20 years after the pandemic, and only return to counterfactual levels after 40 years.

Analysis based on historical data, however, might not be relevant in this case. According to Baker et al. ( 2020 ), COVID‐19 has led to massive spikes in uncertainty, and there are no close historical parallels. Because of the speed of evolution and timely requirements of data, the authors suggest that one should utilize forward‐looking uncertainty measures to ascertain its impact on the economy. They formulate the uncertainty measure from the Standard & Poor's 500 Volatility Index (VIX) and the news‐based economic policy uncertainty (EPU) index developed by Baker et al. ( 2016 ). Using a real business cycle model, the authors find that a COVID‐19 shock leads to a year‐over‐year contraction of GDP by 11% in fourth quarter of 2020. According to the authors, more than half of the contraction is caused by COVID‐19‐induced uncertainty. Based on a similar approach, Altig et al. ( 2020 ) conduct an analysis of different forward‐looking uncertainty measures during the pandemic. Coibion et al. ( 2020a ) use surveys to assess the macroeconomic expectations of households in the United States. They find that it is primarily lockdowns, rather than the infections themselves, that lead to declines in consumption spending and employment, lower inflationary expectations, increased uncertainty, and lower mortgage payments being made.

Eichenbaum et al. ( 2020 ) model the interactions between economic decisions and the spread of the virus. They find that, without any mitigation measures, aggregate consumption falls by 9.3% over a 32‐week period. On the other hand, labor supply or hours worked follow a U‐shaped pattern, with a peak decline of 8.25% in the 32nd week from the start of the pandemic. These reductions decrease peak infection rates and death tolls from 7% and 0.30% to 5% and 0.26% respectively, but worsen the magnitude of the recession. Infected people fail to internalize the impact of their choices on the spread of the virus. Therefore, the optimal containment policy increases the severity of the recession but saves lives. 13

Mulligan ( 2020 ) assesses the opportunity cost of “shutdowns” in order to document the macroeconomic impact of COVID‐19. Within the National Accounting Framework for the United States, the author extrapolates the welfare loss stemming from “nonworking days,” the fall in the labor‐capital ratio resulting from the absence/layoff of workers, and the resulting idle capacity of workplaces. After accounting for dead‐weight losses stemming from fiscal stimulus, the replacement of normal import and export flows with black market activities, and the effect on nonmarket activities (lost productivity, missed schooling for children and young adults), the author finds the welfare loss to be approximately $7 trillion per year of shutdown. Medical innovations, such as vaccine development, contact tracing, and workplace risk mitigation can help to offset the welfare loss by around $2 trillion per year of shutdown.

Other researchers have examined the supply side. Bonadio et al. ( 2020 ) use a quantitative framework to simulate a global lockdown as a contraction in labor supply for 64 countries. The authors find that the average decline in real GDP constitutes a major contraction in economic activity, with a large share attributed to disruptions in global supply chains. Elenev et al. ( 2020 ) model the impact of COVID‐19 as a fall in worker productivity and as a decline in labor supply, which both adversely affect firm revenue. The fall in revenue and the subsequent non‐repayment of debt‐servicing obligations spur a wave of corporate defaults, which might also bring down financial intermediaries. Céspedes et al. ( 2020 ) formulate a minimalist economic model in which the virus also leads to losses in productivity. The authors predict a vicious cycle triggered by the loss of productivity causing lower collateral values, in turn limiting the amount of borrowing activity, subsequently leading to decreased employment, followed by a further decline in productivity. The shock is thus magnified through an “unemployment and asset price deflation doom loop” (see Fornaro & Wolf, 2020 ).

Consumption pattern responses and debt responses from pandemic shocks had not been analyzed prior to COVID‐19 (Baker et al., 2020 ). Using transaction‐level household data, these authors find that households sharply increased their spending during the initial period in specific sectors such as retail and food spending. These increases, however, were followed by a decrease in overall spending. Similarly, Chang and Meyerhoefer ( 2020 ) show that consumers in Taiwan have increased food purchases from online platforms. Binder ( 2020 ) conduct an online survey of 500 United States consumers to investigate their concerns and responses related to COVID‐19, which indicated those items of consumption on which they were spending either more or less. They find that 28% of the respondents in that survey postponed future travel plans, and that 40% forewent food purchases. Interestingly, Binder ( 2020 ) finds from the surveys that consumers tend to associate graver concerns about COVID‐19 with higher inflationary expectations, a sentiment which serves as a proxy for “pessimism” or “bad times.”

Clemens and Veuger ( 2020 ) focus on the declines in government sales and income tax collections across US states. According to the authors, COVID‐19 has led to a substantial decline in consumption levels compared to income levels. This pattern is unlike the case in previous recessions, during which income decreased more than consumption. The authors find that the COVID‐19 pandemic will reduce the states’ tax collections by $42 billion in the second quarter of 2020. For fiscal year 2021, the authors anticipate an overall decline in sales and income tax revenues of $106 billion with heterogenous losses across US states.

McKibbin and Fernando ( 2020 ) estimate the aggregate economic costs. Using a hybrid DSGE/CGE global model, the authors model COVID‐19 as a negative shock to labor supply, consumption spending, financial markets, but as a positive shock to government expenditure, particularly stemming from health‐related expenditures. The authors outline seven different scenarios and provide a range of estimates of the increase in mortality and the fall in GDP for a number of countries across the world. In the case of the most contained outbreak, the number of deaths reaches around 15 million, while the reduction in global GDP is around $2.4 trillion in 2020.

Eppinger et al. ( 2020 ) use a quantitative international trade model with input‐output linkages for 43 countries to assess the impact of COVID‐19 supply shock on global value chains. They find that due to the supply shock, China experienced a welfare loss of 30% with moderate (positive or negative) spillover to other countries. Estimating a simulation consisting of a counterfactual scenario described as “without global value chains,” the authors find that welfare losses are reduced for some countries by as much as 40%, while they are magnified for others.

The economic impact of shocks, such as pandemics, is usually measured with aggregate time series data. However, these datasets are available only after a certain lag. In order to analyze the economic impact at a higher frequency, Lewis et al. ( 2020 ) developed a weekly economic index (WEI) using 10 different economic variables to track the economic impact of COVID‐19 in the United States. These authors report that between March 21 and March 28, the WEI declined by 6.19%. This was driven by a decline in consumer confidence, a fall in fuel sales, a rise in unemployment insurance (UI) claims, and changes in other variables. Similarly, Demirguc‐Kunt et al. ( 2020 ) estimate the economic impact of social distancing measures via three high‐frequency proxies (electricity consumption, nitrogen dioxide emissions, and mobility records). The authors find that social distancing measures led to a 10% decline in economic activity (as measured by electricity usage and emissions) across European and Central Asian countries between January and April. Chetty et al. ( 2020 ) develop a real‐time economic tracker using daily statistics on consumption, employment, business revenue, job postings, and other variables. The authors show that the initial slowdown in economic activity was partly driven by reductions in consumption by high‐income individuals. These spending shocks negatively affected business revenues catering to high‐income individuals. Subsequently, low‐income individuals working for these businesses lose much of their incomes and reduce their consumption levels. Kapteyn et al. ( 2020 ) tracked a representative sample of 7,000 respondents in Los Angeles County, California every 2 weeks to assess the impact of COVID‐19 over time.

Brinca et al. ( 2020 ) estimate the labor demand and supply shocks occurring in different sectors in the US economy employing a Bayesian structural vector autoregression model. They find that the decrease in work hours can be attributed to negative labor supply shock, a result that they suggest has important policy design implications. A negative labor supply shock is directly related to the lockdown and might be mitigated once such policies are lifted.

5. SOCIOECONOMIC CONSEQUENCES OF COVID‐19

We now review studies documenting the socioeconomic consequences of COVID‐19 and the ensuing lockdowns. Social distancing and lockdown measures have been shown to adversely affect labor markets, mental health and well‐being, racial inequality, and gender‐related outcomes. The environmental implications, while likely to be positive overall, also deserve careful analysis.

5.1. Labor market outcomes

A large number of studies document the effects on the variables of hours of work and job losses (e.g., Kahn et al., 2020 ). The major increases in unemployment observed in the United States are driven partly by lockdowns and social distancing policies (Rojas et al., 2020 ). Accounting for cross‐state variation in the timing of business closures and stay‐at‐home mandates in the United States, Gupta et al. ( 2020 ) find that the employment rate in the United States falls by about 1.7 percentage points for every extra 10 days that a state experienced a stay‐at‐home mandate during the period of March 12th to April 12th.

Coibion et al. ( 2020b ) find that the level of unemployment and job losses in the United States is more severe than one might judge based on the rise in UI claims, which is to be expected given the low coverage rate of the UI regimes in the United States. They also project a severe fall in the labor participation rate in the long run accompanied by an increase in the number of “discouraged workers” (jobless workers who have stopped actively searching for work, effectively withdrawing from the labor force). This phenomenon might be due to the disproportionate impact of COVID‐19 on the older population. Aum et al. ( 2020a , 2021b ) find that an increase in infections leads to a drop in local employment even in the absence of lockdowns in South Korea, whose government did not mandate them. This estimated impact was higher for countries, such as the United States and the United Kingdom, where mandatory lockdown measures were imposed.

Adams‐Prassl et al. ( 2020a ) analyze the inequality of the distributions of job and income losses based on the type of job held and on individual characteristics for the United States and the United Kingdom. The authors find that workers who can perform none of their employment tasks from home are more likely to lose their job. This study also finds that younger individuals and people without a university education were significantly more likely to experience drops in their income. Yasenov ( 2020 ) finds that workers with lower levels of education, younger adults, and immigrants are concentrated in occupations whose tasks are less likely to be performed from home. Similarly, Alstadsæter et al. ( 2020 ) find that the pandemic shock in Norway has a strong socioeconomic gradient, as it has disproportionately affected the financially vulnerable population, including parents with younger children.

Béland, Brodeur, and Wright ( 2020 ) discuss heterogeneous effects across occupations and workers in the United States, showing that occupations that have a higher share of workers working remotely were less affected by COVID‐19. On the other hand, occupations with relatively more workers working in proximity to others were more affected. They also find that occupations classified as “more exposed to disease” are less affected, which is possibly due to the number of essential workers in these occupations. Based on these results, it can be reasonably expected that workers might change (or students might select different) occupations in the medium term. Bui et al. ( 2020 ) focus on the impact of COVID‐19 on older workers in the United States. Using CPS data, they show that older workers who are over 65 years of age, especially women, are facing higher unemployment in this COVID‐19 recession compared to previous ones.

Kahn et al. ( 2020 ) show that firms in the United States dramatically reduced job vacancies from the second week of March 2020 and thereafter. The authors find that the job vacancy declines occurred simultaneously with increasing UI claims. Notably, the labor market declines (proxied through reductions in job vacancies and increases in UI claims) were uniform across states, with no notable differences across states which experienced the spread of the pandemic, or implemented stay‐at‐home orders, earlier than others. The study also finds that the reductions in job vacancies were uniform across industries and occupations, except for those in front line jobs, such as nursing. Baert et al. ( 2020a ) investigate the impact of COVID‐19 on career prospects through surveys conducted in Belgium. They document concerns that were expressed about job losses and missing out on promotions, especially among migrant workers.

Fairlie ( 2020 ) analyzes the impact of COVID‐19 on the number of small businesses in the United States. Using the April 2020 CPS data, the author finds that the number of active business owners declined by 22% between February and April 2020. While most major industries faced large drop in business, the authors also find that female and immigrant‐owned businesses were disproportionately affected.

With the enforcement of social distancing measures, work from home has become increasingly prevalent. The degree to which economic activity is impaired by such social distancing measures depends largely on the capacity of firms to maintain business processes from the homes of workers (Alipour et al. 2020 ; Papanikolaou & Schmidt, 2020 ). Additionally, working from home or working remotely are much more common and are thought to cause lower productivity losses in industries that are staffed by better educated and better paid workers (Bartik et al., 2020 ). Brynjolfsson et al. ( 2020 ) find that the increase in cases per 100k individuals is associated with a significant rise in the fraction of workers switching to remote work and the fall in the fraction of workers commuting to work in the United States. Interestingly, the authors find that people working from home are more likely to claim UI (if they are laid off) than people who are still commuting to work and are likely working in industries providing essential services.

Dingel and Neiman ( 2020 ) analyze the feasibility of jobs that can be done from home. They find that 37% of jobs can be feasibly performed from home. A different but related context for the feasibility of work from home is the extent to which the job involves face‐to‐face (F2F) interaction. According to Avdiu and Nayyar ( 2020 ), the job‐characteristic variables of home‐based work (HBW) and F2F interaction differ along three main dimensions, namely: (i) temporal (short run vs. medium run); (ii) the primary channel of effects (supply and demand of labor for the occupation/tasks); and (iii) the relevant margins of adjustment (intensive vs. extensive). They argue that the supply of labor in industries with HBW capabilities and low F2F interactions (e.g., professional, scientific, and technical services) might be the least affected. Nevertheless, those industries and occupations with HBW capabilities and high F2F interactions are likely to experience negative productivity shocks. As lockdown restrictions are lifted, industries with low HBW capabilities and low F2F interactions (e.g., manufacturing, transportation, and warehousing) might be able to recover relatively quickly. The risk of infection through physical proximity can be mitigated by wearing personal protective equipment (PPE) and by taking other relevant precautionary measures. However, those industries with low HBW capabilities and high F2F interactions (e.g., accommodation and food services, arts entertainment and recreation) are likely to experience slower recoveries, as consumers might be apprehensive about patronizing them, for example, cinemas and restaurants. Using a web survey in Belgium, Baert et al. ( 2020b ) find that a majority of respondents thought that teleworking and digital conferencing were here to stay and will become more common in the postpandemic period.

From the firm's perspective, there are large short‐term effects of temporary closures, such as the (perhaps permanent) loss of productive workers and declines in job postings, all of which are characterized by strong heterogeneity across industries. Bartik et al. ( 2020 ) survey a small number of firms in the United States and document that several of them have temporarily closed shop and reduced their number of employees compared to January 2020. The surveyed firms were not optimistic about the efficacy of the fiscal stimulus implemented by the federal government of the United States. Campello et al. ( 2020 ) find that job losses have been more severe for industries with highly concentrated labor markets (i.e., where hiring is dominated by a few employers), nontradable sectors (e.g., construction, health services), and credit‐constrained firms. Hassan et al. ( 2020 ) discern a pattern of heterogeneity with respect to firm resilience across industries around the World. Based on earnings call reports, they provide evidence that some firms are expecting increased business opportunities in the midst of the global disruption (e.g., firms which make medical supplies or others whose competitors are facing negative impressions after the outbreak due to their association with regions where case numbers are high). Barrero et al. ( 2020 ) measure the reallocation of labor in response to the pandemic‐induced demand response (e.g., increased hiring by delivery companies, delivery‐oriented restaurant/fast food chains, technology companies).

To conclude this subsection, a large number of studies try to predict labor market outcomes by exploiting high frequency data (e.g., Adams‐Prassl et al. 2020a , Chetty et al., 2020 ). For instance, Bartik et al. ( 2020 ) and Kurmann et al. ( 2020 ) rely on worker‐firm matched daily data drawn from “Homebase,” a scheduling and time clock software provider, to construct real‐time data for small businesses. Other studies have also used high‐frequency electricity market data to estimate the short‐run impacts of COVID‐19 on economic activity (e.g., Fezzi & Fanghella, 2020 ).

5.2. Health outcomes

The impact of the pandemic on physical health and mortality has been documented in many studies (e.g., Goldstein & Lee, 2020 ; Lin & Meissner, 2020 ). Knittel and Ozaltun ( 2020 ) document a positive correlation between the share of elderly population, the incidence of commuting via public transportation, and the number of COVID‐19 deaths in the United States. In contrast, the authors provide evidence that obesity rates, the number of ICU beds per capita, and poverty rates are not related to the death rate. Chatterji and Li ( 2021 ) document the effect of the pandemic on the US health care sector. The authors find that it is associated with a 67% decline in the total number of outpatient visits per provider by the week of April 12th‐18th 2020 relative to the same week in prior years. This might have negative health consequences, especially among individuals with chronic health conditions. Hermosilla et al. ( 2020 ) show that COVID‐19 has crowded out non‐COVID‐19‐related health care demands in China. Others, such as Alé‐Chilet et al. ( 2020 ), explore the drop in emergency cases in hospitals around the world.

Nevertheless, during a crisis, such as the COVID‐19 pandemic, it is common for everyone to experience increased levels of distress and anxiety, particularly the sentiment of social isolation (American Medical Association, 2020 ). A growing number of studies document worsening mental health status and levels of well‐being (Adams‐Prassl et al. 2020b ; Brodeur, Clark, Fleche, & Powdthavee, 2021 ; Davillas & Jones, 2020 ; de Pedraza et al., 2020 , and Tubadji et al., 2020 ). According to Lu et al. ( 2020 ), social distancing or lockdown measures are likely to affect psychological well‐being through a lack of access to essential household supplies, discriminatory treatment, or exclusion by neighbors. They assert that maintaining a positive attitude (in terms of severity perceptions, the credibility of real‐time updates of information, and confidence in social distancing measures) can help reduce depression. Hamermesh ( 2020 ) also provides evidence that, adjusted for numerous demographic and economic variables, happiness levels during the COVID‐19 pandemic are affected by how people spend time and with whom. In the opposite case, using an experimental set‐up, Bogliacino et al. ( 2020 ) find that a negative shock triggered by COVID‐19 lowers cognitive functionality and increases risk aversion and the propensity to punish others, that is negative reciprocity. Public mental health is also affected by the cognitive bias related to the diffusion of public death toll statistics (Tubadji et al., 2020 ). These needs are all the less likely to be addressed given the lower levels of provision of health care and social work services.

Using the Canadian Perspective Survey Series, Béland, Brodeur, Mikola, and Wright ( 2020 ) find that those who missed work not due to COVID‐19, and those who were already unemployed, showed declines in mental health. Using panel data in the United Kingdom, Etheridge and Spantig ( 2020 ) report a large deterioration in the state of mental health, with much larger effects for women.

The implementation of lockdown policy also adversely affected public mental health. Armbruster and Klotzbücher ( 2020 ) demonstrate that there were increases in the demand for psychological assistance (through helpline calls) due to lockdown measures imposed in Germany. The authors find that these calls were mainly driven by mental health issues such as loneliness and depression. Brodeur, Clark, Fleche, & Powdthavee et al. ( 2021 ) show that there has been a substantial increase in the search intensity on Google for “boredom” and “loneliness” during the postlockdown period in nine Western European countries and the United States during the first few weeks of lockdowns. Using experimental surveys, Codagnone Bogliacino, Gómez, and Charris et al. ( 2020 ) find that about 43% of the population in Italy, Spain, and United Kingdom are at high risk of developing mental health problems; not only because of the negative economic shock, but also due to conditions of long‐standing economic weakness and vulnerability in those countries.

Fetzer et al. ( 2020 ) find that there has been broad public support for COVID‐19 containment measures. However, some of the respondents believe that the general public fails to adhere to health measures, and that the governmental response has been insufficient. These respondents have a tendency to exhibit a poorer state of mental health. If governments are seen to take decisive actions, however, then the respondents altered their perceptions about governments and other citizens, which in turn improved their state of mental health.

5.3. Gender and racial inequality

A growing literature points out that COVID‐19 has had an unequal impact between genders and across races in OECD countries; specifically, women and racial minorities, such as African‐Americans and Latinos, have been unduly and adversely affected. While it is thought that prior recessions typically affected men more than women, many studies provide evidence that COVID‐19 has large negative effects on women's labor market outcomes (Adams‐Prassl et al., 2020a ; Forsythe et al., 2020 ; Yasenov, 2020 ). Alon et al. ( 2020 ) argue that women's employment is concentrated in sectors such as health care and education. Moreover, the closure of schools and daycare centers led directly to increased childcare needs, which would have a negative impact on working and/or single mothers. For example, based on household surveys in Spain, Farré et al. ( 2020 ) find that while men increased their participation in household work and childcare duties during lockdowns, the burden of these tasks fell disproportionately on women.

Couch et al. ( 2020a ) examine the variation in unemployment shocks among minority groups in the United States. The authors find that Latino groups were disproportionately affected by the pandemic. They attribute the difference to an unfavorable occupational distribution (e.g., Latino workers tend to work in nonessential services) and to lower skill levels among them. Borjas and Cassidy ( 2020 ) determine that the COVID‐19 shock led to a fall in employment rates of immigrant men compared to native men in United States, which was in contrast to the historical pattern observed during previous recessions. The immigrants’ relatively high rate of job loss was attributed to the fact that immigrants were less likely to hold jobs that could be performed remotely from home. The likelihood of being unemployed during March 2020 was significantly higher for racial and ethnic minorities in the United States (Montenovo et al., 2020 ). In a similar vein, McLaren ( 2020 ) finds that minorities’ population shares in a county strongly correlate with COVID‐19‐related deaths in the United States. After controlling for the factors of education, jobs, and travel patterns, the correlation holds for the African‐American and the First‐Nations populations. The author shows that these racial disparities between African‐Americans, First Nations peoples, and others can be partially attributed to differentials in public transit usage patterns.

Couch et al. ( 2020b ) find that COVID‐19 has unduly affected women compared to men in the United States. Using employment to population ratios and number of hours from the CPS data, the authors find that women with school‐age children faced greater declines in employment and work hours compared to men between April and August 2020. The reductions in work hours and employment can be explained by additional childcare responsibilities, job and skill characteristics, and lower numbers of women involved in “essential” industries.

Schild et al. ( 2020 ) find that COVID‐19 occasioned a rise of Sino‐phobia across the internet, particularly when western countries started showing signs of infection. Bartos et al. ( 2020 ) document the causal effect of economic hardships on hostility against certain ethnic groups in the context of COVID‐19 using an experimental approach. The authors find that the COVID‐19 pandemic magnifies sentiments of hostility and discrimination against foreigners, especially those from Asia.

5.4. Environmental outcomes

The global lockdown and the considerable slowdown of economic activities are expected to have a positive effect on the environment (Almond et al., 2020 ; Cicala et al., 2020 ). He et al. ( 2020 ) show that lockdown measures in China led to a remarkable improvement in air quality. The Air Quality Index and the fine particulate matter (PM 2.5 ) concentrations were brought down by 25% within weeks of the lockdown, with larger effects recorded in colder, richer, and more industrialized cities. Similarly, Almond et al. ( 2020 ) focused on air pollution and the release of greenhouse gases in China during the post‐COVID‐19 period. They determined that, while nitrogen dioxide (NO 2 ) emissions fell precipitously, sulphur dioxide emissions (SO 2 ) did not decrease. For China as a whole, PM 2.5 emissions fell by 22%; however, ozone concentrations increased by 40%. These variations show that there is not necessarily an unambiguous improvement in air pollution due to the economic slowdown. The reduction can be attributed to less travel in personal vehicles causing lower nitrous oxide (NO 2 ) emissions.

Brodeur, Cook, & Wright ( 2021 ) examine the causal effect of “safer‐at‐home” policies on air pollution across US counties. They find that “safer‐at‐home” policies decreased air pollution (measured as PM 2.5 emissions) by almost 25% on average, with larger effects for populous counties. Cicala et al. ( 2020 ) focus on the health and mortality benefits of reduced vehicle travel and electricity consumption in the United States due to stay‐at‐home policies, suggesting that reductions in emissions from less travel and from lower electricity usage reduced deaths by over 360 per month.

On the other hand, Andree ( 2020 ) focuses on the effect of pollution on cases, finding that PM 2.5 levels are a highly significant predictor of COVID‐19 incidence using data from 355 municipalities in the Netherlands. In terms of COVID‐19‐related deaths, Knittel and Ozaltun ( 2020 ) find no evidence that pollution levels are related to death rates in the United States.

Based on the research discussed in section  5 above, Table  3 provides a summary of these strands of the literature dealing with the socioeconomic and environmental outcomes resulting from social distancing actions, stay‐at‐home orders, and/or lockdowns including a listing of the statistical measures and methodologies that were utilized.

Socioeconomic outcomes of COVID‐19 lockdowns: Summary of studies

6. POLICY MEASURES

The economic literature deals with a wide assortment of policy measures. We organize our presentation into six broad topics: (i) the types of policy measures, (ii) the determinants of government policy, (iii) optimal testing methods, (iv) the lockdown measures and their associated factors, (v) the lifting of the lockdown measures, and (vi) the economic stimulus measures.

To mitigate the negative effects of public health controls on the economy and to sustain and promote public welfare, governments all around the World have implemented a variety of policies within a very short time frame. These include fiscal, monetary, and financial policy measures (Gourinchas, 2020 ). The economic measures vary across counties in terms of breadth and scope, and they target households, firms, health systems, and/or banks (Weder di Mauro, 2020 ).

Using a database of economic policies implemented by 166 countries, Elgin et al. ( 2020 ) employ the technique of Principal Component Analysis (PCA) to develop their COVID‐19 Economic Stimulus Index. The authors correlate the standardized index with predictors of governmental response, such as population characteristics (e.g., median age), public health‐related measures (e.g., the number of hospital beds per capita), and economic variables (e.g., GDP per capita). They find that the economic stimulus is larger for countries with higher COVID‐19 infections, older median ages, and higher GPD‐per‐capita levels. In addition, the authors develop a “Stringency Index,” which includes measures such as school closures and travel restrictions. They find that the “Stringency Index” is not a significant predictor of their economic stimulus index, which suggests that public health measures do not drive economic stimulus measures (Weder di Mauro, 2020 ).

On a similar note, Porcher ( 2020 ) has created an index of public health measures using the PCA technique. The index is based on 10 common public health policies implemented across 180 countries to mitigate the spread of COVID‐19. The index is designed to measure the stringency of the public health response across countries. The author finds that, abstracting from the COVID‐19 case numbers and deaths, countries which have better public‐health systems and effective governance tend to have less stringent public health measures.

C. Cheng et al. ( 2020 ) develop the “CoronaNet–COVID‐19 Government Response Database,” which accounts for policy announcements made by countries globally since 31 December 2019. The information that is contained in this data base is categorized according to: (i) type of policy, (ii) national versus subnational enforcement, (iii) people and geographic region targeted by the policy, and (iv) the time frame within which it is implemented. Table  4 provides a description of the government response database for 125 countries. Counts are tabulated according to 15 types of interventions for two variables: cumulative number of policies (of that type) implemented and the number of countries which have implemented it. It also displays that average value for the degree of enforcement.

Summary statistics of COVID‐19 government response dataset

Source: C. Cheng et al. ( 2020 ).

There is substantial variation across policy measures. The policy most governments have implemented is “external border restriction,” that is, restricting access to entry through ports. It has been imposed by 186 countries; the second most common policy measure, implemented by 169 countries, is “school closures.” However, in terms of the frequency of implementation across all countries, the type of “obtaining or securing health resources” has the highest level. This includes the provision of materials (e.g., face masks), personnel (e.g., doctors, nurses), and infrastructure (e.g., hospitals). The second most frequently implemented policy is “restrictions on nonessential businesses.” In terms of stringency of policy enforcement, “emergency declaration” and the formation of a “new task force” or an “administrative reconfiguration to tackle pandemic” are implemented with 100% stringency.

Due to these major differences between policy responses across countries and over time, the authors use a dynamic Bayesian item‐response approach to measure the implied economic, social, and political cost of implementing a particular policy over time. They also develop a supplementary measure labelled the “Policy Activity Index,” which assigns a higher rank for policy measures to countries that are more willing to implement a “costly” policy. Based on that index, the authors determine that school closure is the costliest to implement followed by mandatory business closure and social distancing policies. Moreover, internal border restrictions are viewed as more costly compared to external border restriction.

The topic of optimal testing methods has received a great deal of attention in the media and, to some extent, in academia. A well‐known proposal defended by Paul Romer and many others is a comprehensive “test and isolate” policy, which would effectively reduce the effective reproduction number and allow the economy to operate more openly. 14 Taipale et al. ( 2020 ) formalize this proposal and argue that the epidemic would collapse at a sufficient rate of testing and isolation, and that concurrent testing would outperform random sampling of individuals. Other proposals for optimal testing include regular testing of people in groups that are more likely to be exposed to COVID‐19 (e.g., Cleevely et al., 2020 ; Gollier & Gossner, 2020 ), multi‐stage group testing (e.g., Eberhardt et al., 2020 ), and testing on exit from quarantine instead of upon entry (e.g., Wells et al. 2020 ).

The topic of optimal lockdown policies has been investigated mostly by using epidemiology macroeconomic models, some of which are oriented around the dichotomy between the case in which the choices (and responses) are all made by private agents and the case in which the choices are made by a social planner (Acemoglu et al., forthcoming; Alvarez et al., forthcoming; Berger et al., forthcoming; Bethune & Korinek 2020 ; Eichenbaum et al., 2020 ). Jones et al. ( 2020 ) argue that in contrast to private agents, the social planner will seek to front‐load mitigation strategies: that is to impose strict lockdowns from the beginning to reduce the spread of infection and let the economy to fall into a deep recession. This is because their model's set‐up not only considers the concomitant health care costs and congestion in hospitals, but also rightly considers the fact that workers need time to become productive for a work‐from‐home situation. The outcomes are dependent on the assumed values of the parameters that are inputted into these models. The optimal policy choice reflects the rate of time preference, epidemiological factors, the value of statistical life, the rate at which death rate increases in the infected population, the hazard rate for a vaccine discovery, the learning effects in the health care sector, and the severity of output losses due to a lockdown (Gonzalez‐Eiras & Niepelt, 2020 ). The intensity of the lockdown depends on the gradient of the fatality rate as a function of the number of infected individuals and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown and shortens the duration of the optimal lockdown (Alvarez et al., forthcoming). Chang and Velasco ( 2020 ) argue that the optimality of policies depends on peoples’ expectations. For instance, fiscal transfers must be large enough to induce people to stay at home in order to reduce the degree of contagion; otherwise they might not change their behavior in efforts to reduce the risk of infection. Their analysis also contains a critique of the use of SIR models, as the parameters used in that class of models (which remain fixed in value) would shift as individuals change their behavior in response to policy. Kozlowski et al. ( 2020 ) investigate the scarring effect on perceptions (i.e. the change in belief about the probability of an extreme but negative or tail‐risk event) of COVID‐19, and find that revisions in belief about tail‐risk events among economic agents will lead to a larger and more persistent negative impact on the economy in the long run.

When the daily death rates and case numbers decline, policies regarding reopening the economy are of primary importance. Gregory et al. ( 2020 ) describe the lockdown measure as a “loss of productivity,” whereby relationships between employers and laborers are suspended, terminated, or continued. They further explain that postpandemic, the speed and the type (V‐shaped or L‐shaped) of recovery depend on at least three factors: (i) the fraction of workers who, at the beginning of the lockdown, enter unemployment while maintaining a relationship with their employer, (ii) the rate at which inactive relationships between employers and employees dissolve during the lockdown, and (iii) the rate at which workers who, at the end of the lockdown, are not recalled by their previous employer can find new, stable jobs elsewhere (Gregory et al., 2020 ).

Harris ( 2020 ) points out the importance of utilizing several status indicators (e.g., results of partial voluntary testing, guidelines for eligibility of testing, daily hospitalization rates) in order to decide upon the course of action on reopening the economy. Kopecky and Zha ( 2020 ) state that decreases in deaths are either due to implementation of social distancing measures or to herd immunity; it is hard to identify and disentangle those factors using standard SIR models. They argue that with the “identification problem,” there will be considerable uncertainty about the conditions for restarting the economy. Only comprehensive testing can help resolve this ambiguity by quickly and accurately identifying new cases so that future outbreaks could be contained by isolation and contact‐tracing measures (Kopecky & Zha, 2020 ).

Agarwal et al. ( 2020 ) rely on synthetic control methods to investigate the effect of counterfactual mobility restriction interventions in United States. Using the daily death data from different countries, the authors create different “synthetic mobility United States” variables. These are applied to predict a counterfactual scenario and to understand the trade‐off between different levels of mobility interventions on death levels in United States. They find that a small decrease in mobility reduces the number of deaths; however, after registering a 40% drop in mobility, the benefits derived from mobility restrictions (in terms of the number of deaths) diminish. Using a counterfactual scenario, the authors find that lifting severe mobility restrictions but retaining moderate mobility restrictions (e.g., by imposing limitations in retail and public transport locations) might effectively reduce the number of deaths in United States. Others, such as Rampini ( 2020 ), make the case for the sequential lifting of lockdown measures for the younger population at the initial stages, followed by the older population at later stages. The authors state that the lower effect on the younger population group is a fortunate coincidence, and thus, the economic consequences of interventions can be greatly reduced by adopting a sequential approach. Oswald and Powdthavee ( 2020 ) make a similar case for releasing the younger population from mobility restrictions first on the grounds of higher economic efficiency (as they are more likely to be in the labor force) and their greater resilience against infections.

As some US states reopened, some researchers turned their focus on the immediate consequences. Nguyen et al. ( 2020 ) find that 4 days after reopening, mobility has increased by 6 to 8%, with greater increases across states which were late adopters of lockdown measures. These findings have important implications for the resurgence of cases, hospital capacity utilization, and further deaths. Dave, Friedson, Matsuzawa, and McNichols et al. ( 2020 ) analyze the effect of lifting the shelter‐in‐place order in Wisconsin, after the Wisconsin Supreme Court abolished it, on social distancing and the number of cases and find no statistically significant impact. W. Cheng et al. ( 2020 ) find that employment activity in the United States increased in May due to reopening in some states, mainly as a result of people who resumed working at their previous job. However, they find that the longer employees are separated from their firms, the more their re‐employment probabilities decline.

In regards to the aggregate macroeconomy, Gourinchas ( 2020 ) states that without substantial, timely, and stimulative macroeconomic intervention, the output lost from the economic downturn will be greatly amplified, especially as economic agents react to the negative shock by reducing consumption spending, investment spending, and engaging in lower credit transactions. The author suggests that there should be cross‐regional variation in government responses based on country characteristics. With high amounts of government debt and historically low interest levels existing in most developed countries, Bianchi et al. ( 2020 ) recommend a coordinated monetary and fiscal policy to address the COVID‐19 economic fallout. They recommend that fiscal policy should be used to enact an emergency budget with a ceiling placed on the debt‐to‐GDP ratio. This measure would increase aggregate spending, raise the inflation rate, and reduce real interest rates. The monetary authorities would need to coordinate with fiscal policy authorities by adopting an above‐normal inflation target. In the long run, governments would try to balance the budget, and future monetary policy would aim to bring inflation back to normal levels.

Bigio et al. ( 2020 ) focus on the cases for government transfers versus credit subsidy policies. They determine that the optimal mix between them depends on the level of financial development in the economy. According to these authors, economies with a developed financial system should utilize stimulative credit policies. On the other hand, developing economies should engage in more transfer spending. Guerrieri et al. ( 2020 ) show that the optimal economic policy response for the “Keynesian supply shock” induced by COVID‐19 would be to combine expansionary monetary policy and bolster social insurance programs for employees in the affected sectors. Unconventional policies, such as wage subsidies, helicopter drops of liquid assets, equity injections, and loan guarantees, can keep the economy in a full employment, high‐productivity equilibrium (Céspedes et al., 2020 ). These policies can break the cycle of negative feedback loops between corporate defaults and potential insolvency of financial intermediaries, which could culminate in a meltdown in financial markets (Elenev et al., 2020 ). Didier et al. ( 2020 ) discuss the policy menu, priorities, and trade‐offs of providing direct financing to firms.

Chetty et al. ( 2020 ) analyze the causal effect of policies implemented in the United States on households and businesses. They find that stimulus payments delivered through the Coronavirus Aid, Relief, and Economic Security (CARES) Act increased consumption spending, and that this spending was directed toward durable goods, which require low physical interaction at various stages of production. As a result, this spending is not directed toward small‐ and medium‐size businesses whose revenues were very adversely affected. On the other hand, loans to small businesses from the Paycheck Protection Program did little to restore employment among businesses. According to their analysis, the economic recovery depends on restoring consumer confidence and targeting income replacement programs rather than uniform lump‐sum stimulus payments.

Codagnone, Bogliacino, and Gómez, Folkvord et al. ( 2020 ) focus on the expectations of stakeholders with regards to the postlockdown period. Using an experimental survey in Spain, Italy, and United Kingdom, the authors find that exposure to the COVID‐19 shock and the ensuing lockdown led to pessimistic expectations about job opportunities, greater drawdowns of savings than before, and a deterioration in social relations which might be instrumental in finding job opportunities in the long run. The authors conclude that the fiscal policy measures might be insufficient in managing these expectations amidst uncertainties. They call on policy makers to draft contingency plans for exiting the lockdown—not only in terms of public expenditures earmarked for postlockdown operations, but also in terms of public health strategies to tackle a second wave of COVID‐19.

7. CONCLUSIONS

This study delved into the research related to the economics of COVID‐19 that has been released over a short time period. Our primary aim is to synthesize and to bring coherence and structure to the very rapidly growing body of relevant scientific evidence. By providing an annotated list of dozens of articles along with a brief capsule of their content, we hope to facilitate further research in the many strands of the COVID‐related literature. For readers who are interested in this critically important and pressing topic, this piece also provides an informative summary of the state of knowledge at the time of writing.

Before covering the impacts of COVID‐19, we documented the most popular data sources that are exploited to measure the known cases and deaths resulting from COVID‐19, as well as the social distancing activities. We first pointed out that the numbers of reported cases and deaths are subject to measurement error due to many factors, including testing capacity constraints and lags. Mobility measures that are based on GPS coordinates emitted from cell phones have been used extensively to measure social distancing. However, there are certain caveats that apply, particularly in terms of privacy concerns and the representativeness of data. The article also reviewed separate research related to social distancing activity itself, particularly in regards to its determinants, its efficacy in mitigating the spread of COVID‐19, and compliance with these orders. Going forward, social distancing actions and their measurements will continue to figure prominently in academic research and policy development.

We divided our coverage of the impact of the macroeconomy into two subsections, the first of which deals with the propagation mechanisms. The stay‐at‐home orders have very adverse effects on supply chains as well as on employment, which in turn causes drastic declines in consumption spending for many goods and services. The resultant declines in consumer and investor confidence reinforce negative multiplier effects in a downward spiral between labor and output markets, which can be partially attenuated by stimulative fiscal and monetary policies. Since the trajectory for the macroeconomy depends critically on the degree of spread of the virus itself, some researchers have integrated that element into their models. We reviewed the three potential “shapes” for the macroeconomic recovery: the highly optimistic yet implausible “V” path, the somewhat favorable “U” path, and the pessimistic yet more likely “L” path.

The second aspect of the macroeconomic impact of COVID‐19 that we discussed involves the forecasts. It is thought that the lockdown and social distancing measures wreak greater economic harm than the spread of the virus itself. The tremendous uncertainty regarding the path of the virus is compounded with a high degree of economic uncertainty such that these projections are subject to very wide confidence intervals and constant revisions. Some articles have attempted to address the longer‐term negative impacts on macro variables such as capital formation, productivity, and government finances. Other studies have focused on changes in patterns of consumption, employment, savings, and consumer debt by exploiting real‐time data.

In terms of the socioeconomic consequences of COVID‐19, we focus on the impact of the pandemic and the social distancing measures on outcomes in four areas: the labor market, mental health and well‐being, racial and gender inequality, and the environment. In terms of the labor market outcomes, research has shown that there is a high degree of heterogeneity in the pattern of job losses. The pandemic has caused a major shift toward work from home and away from positions involving F2F interactions with either the public or coworkers. Due to technological features and the nature of the services rendered, there are only a certain number of jobs that can be “feasibly” done from home and do not require F2F interactions. This contributes to the disproportionate effect of the pandemic on workers in certain industries and occupations, many of which have a relatively high concentration of lower‐skilled and/or less educated workers.

Social distancing measures have led to serious deteriorations in the levels of mental health, family stress, and domestic violence. Health care services for non‐COVID patients have been crowded out in many instances. There has been a marked rise in observed racial discrimination and sentiments of hostility toward certain ethnic groups. A growing number of studies also document that women have been adversely affected by the loss of child care and educational services for their children. The only seemingly positive consequence of social distancing/lockdown measures is the decrease in air pollution levels and the incidence of accidents involving motor vehicles. However, the impacts on the environment are multi‐faceted, and thus there remains a fair amount of ambiguity.

The goal of our piece was to survey and summarize the findings of the literature on the economics of COVID‐19. This was a very challenging task, as the literature is growing and evolving fast, and the pandemic is far from over at the time of writing. There are a few qualifications that are worth mentioning. First, very few of the research articles surveyed have undergone normal scientific review processes. Second, we mostly did not comment on methodology, which necessitates caution in interpretations. Finally, due to time as well as space constraints, we offer little in the way of critical analysis. Nonetheless, we hope this survey will facilitate further research in the many strands of the COVID‐related literature.

Supporting information

Table A: Major Pandemics: Historical Timeline

Table B: COVID‐19 ‐ Timeline

Figure A: Cumulative COVID‐19 Cases and Deaths – Global Pandemic (as on 30 November 2020)

Table C: Cumulative Cases: Top 10 Countries (as of 30 November 2020)

Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID‐19. Journal of Economic Surveys. 2021;35:1007–1044. 10.1111/joes.12423

Social distancing (or physical distancing) is defined as maintaining physical space between yourself and other people residing outside one's home. To practice social/physical distancing: (i) stay at least 6 feet (about 2 arms’ lengths) from other people, (ii) do not gather in groups, and (iii) avoid crowded places and mass gatherings.

The list of NBER working article is available at this URL: https://www.nber.org/wp_covid19.html

The list of IZA discussion articles is available at this URL: https://covid‐19.iza.org/publications

See the link for the numbers and visual representation. Retrieved from https://coronavirus.jhu.edu

See WHO COVID‐19 Dashboard: https://covid19.who.int.

Refer to Johns Hopkins University ( 2020b ) for CFR data across countries.

See the link for further details: https://ourworldindata.org/mortality‐risk‐covid .

See the link for further details: https://covidtracking.com/data .

See the link for further details: https://covidtracking.com/race .

Mobility measures track work locations based on movements to a workplace from a reference point such as their home. However, if a person works from home or becomes unemployed, there will not be a distinct workplace reference point. Hence, the quality of mobility measures is expected to deteriorate.

The WHO Health System Response Monitor provides a cross country analysis and other details: https://analysis.covid19healthsystem.org/ .

According to the authors, if all members of a set choose to implement shelter‐in‐place policies, then the best response for agents is to follow. Hence, even in the absence of a federal mandate, the members of this “tipping set” can drive all others to adopt shelter‐in‐place policies.

The interaction between economic and epidemiological models is described in more details in the Online Appendix.

Further details on the “test and isolate” policy is available at the URL: https://paulromer.net/covid‐sim‐part1.

See the link for further details: https://www.google.com/covid19/mobility .

See the link for further details: https://www.unacast.com/covid19 .

See the link for further details: https://www.safegraph.com/dashboard/covid19‐commerce‐patterns .

See the link for further details: http://research.baidu.com/Blog/index‐view?id=13 .

• Abouk, R. , & Heydari, B. (2021). The immediate effect of COVID‐19 policies on social distancing behavior in the United States. Public Health Reports. 10.1177/0033354920976575 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Acemoglu, D. , Chernozhukov, V. , Werning, I. , & Whinston, M. D. (forthcoming). A multi‐risk sir model with optimally targeted lockdown. American Economic Review: Insights .
• Adams‐Prassl, A. , Boneva, T. , Golin, M. , & Rauh, C. (2020a). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189, 104245. [ Google Scholar ]
• Adams‐Prassl, A. , Boneva, T. , Golin, M. , & Rauh, C. (2020b). The impact of the coronavirus lockdown on mental health: Evidence from the US. Cambridge Working Papers in Economics. University of Cambridge, Cambridge, MA. Retrieved from http://www.econ.cam.ac.uk/researchfiles/repec/cam/pdf/cwpe2037.pdf
• Agarwal, A. , Alomar, A. , Sarker, A. , Shah, D. , Shen, D. , & Yang, C. (2020). Two burning questions on COVID‐19: Did shutting down the economy help? Can we (Partially) reopen the economy without risking the second wave? arXiv.org, 2005.00072. Retrieved from http://arxiv.org/abs/2005.00072
• Akesson, J. , Ashworth‐Hayes, S. , Hahn, R. , Metcalfe, R. D. , & Rasooly, I. (2020). Fatalism, beliefs, and behaviors during the COVID‐19 pandemic. NBER Working Paper No. 27245. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Aksoy, C. G. , Eichengreen, B. , & Saka, O. (2020). The political scar of epidemics. IZA Discussion Paper No. 13351. Institute of Labor Economics, Bonn, Germany. [ Google Scholar ]
• Aksoy, C. G. , Ganslmeier, M. , & Poutvaara, P. (2020). Public attention and policy responses to COVID‐19 pandemic. IZA Discussion Paper No. 13427. Institute of Labor Economics, Bonn, Germany. http://ftp.iza.org/dp13427.pdf [ Google Scholar ]
• Alé‐Chilet, J. , Atal, J. P. , & Dominguez‐Rivera, P. (2020). Activity and the incidence of emergencies: Evidence from daily data at the onset of a pandemic. PIER Working Paper 20‐016. University of Pennsylavania, Philadelphia, PA. Retrieved from https://economics.sas.upenn.edu/system/files/working‐papers/20‐016%20PIER%20Paper%20Submission%20_NEW.pdf
• Alipour, J.‐V. , Falck, O. , & Schüller, S. (2020). Germany's capacities to work from home. IZA Discussion Paper No. 13152. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13152.pdf [ Google Scholar ]
• Allcott, H. , Boxell, L. , Conway, J. C. , Gentzkow, M. , Thaler, M. , & Yang, D. Y. (2020). Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics, 191, 104254. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Almond, D. , Du, X. , & Zhang, S. (2020). Did COVID‐19 improve air quality near Hubei? NBER Working Paper No. 27086. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Alon, T. M. , Doepke, M. , Olmstead‐Rumsey, J. , & Tertilt, M. (2020). The impact of COVID‐19 on gender equality. NBER Working Paper No. 26947. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Alstadsæter, A. , Bratsberg, B. , Eielsen, G. , Kopczuk, W. , Markussen, S. , Raaum, O. , & Røed, K. (2020). The first weeks of the Coronavirus crisis: Who got hit, when and why? Evidence from Norway. NBER Working Paper No. 27131. National Bureau of Economic Research, Cambridge, MA. 10.3386/w27131 [ DOI ] [ Google Scholar ]
• Altig, D. , Baker, S. R. , Barrero, J. M. , Bloom, N. , Bunn, P. , Chen, S. , Davis, S. J. , Leather, J. , Meyer, B. H. , Mihaylov, E. , Mizen, P. , Parker, N. B. , Renault, T. , Smietanka, P. , & Thwaites, G. (2020). Economic uncertainty before and during the COVID‐19 pandemic. Journal of Public Economics, 191, 104274. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alvarez, F. E. , Argente, D. , & Lippi, F. (forthcoming). A simple planning problem for covid‐19 lockdown, testing and tracing. American Economic Review: Insights. [ Google Scholar ]
• American Medical Association . (2020). Managing mental health during COVID‐19. American Medical Association. Retrieved https://www.ama‐assn.org/delivering‐care/public‐health/managing‐mental‐health‐during‐covid‐19 [ Google Scholar ]
• Andersen, M. , Maclean, J. C. , Pesko, M. F. , & Simon, K. I. (2020). Effect of a federal paid sick leave mandate on working and staying at home: Evidence from cellular device data. NBER Working Paper No. 27138. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Andersen, T. M. , Schröder, P. J. H. , & Svarer, M. (2020). Designing Reopening Strategies in the Aftermath of COVID‐19 Lockdowns: Some Principles with an Application to Denmark. IZA Policy Paper No. 158. Institute of Labor Economics, Bonn, Germany. [ Google Scholar ]
• Andree, B. P. J. (2020). Incidence of COVID‐19 and connections with air pollution exposure: Evidence from the Netherlands. Policy Research Working Paper No. 9221. The World Bank, Washington, DC. Retrieved from http://documents.worldbank.org/curated/en/462481587756439003/Incidence-of-COVID-19-and-Connections-with-Air-Pollution-Exposure-Evidence-from-the-Netherlands [ Google Scholar ]
• Armbruster, S. , & Klotzbücher, V. (2020). Lost in lockdown? COVID‐19, social distancing, and mental health in Germany. Discussion Paper No. 2020–04. University of Freiburg, Wilfried Guth Endowed Chair for Constitutional Political Economy and Competition Policy, Germany. [ Google Scholar ]
• Aum, S. , Lee, S. Y. , & Shin, Y. (2020). COVID‐19 doesn't need lockdowns to destroy jobs: The effect of local outbreaks in Korea. NBER Working Paper No. 27264. National Bureau of Economic Research, Cambridge, MA. [ DOI ] [ PMC free article ] [ PubMed ]
• Aum, S. , Lee, S. Y. , & Shin, Y. (2021). Inequality of fear and self‐quarantine: Is there a trade‐off between GDP and public health? Journal of Public Economics, 194, 104354. 10.1016/j.jpubeco.2020.104354 [ DOI ] [ Google Scholar ]
• Avdiu, B. , & Nayyar, G. (2020). When face‐to‐face interactions become an occupational hazard: Jobs in the time of COVID‐19. Policy Research Working Paper No. 9240. The World Bank, Washington, DC. Retrieved from http://documents.worldbank.org/curated/en/173701589222966874/When-Face-to-Face-Interactions-Become-an-Occupational-Hazard-Jobs-in-the-Time-of-COVID-19 [ Google Scholar ]
• Baccini, L. , & Brodeur, A. (2021). Explaining governors’ response to the Covid‐19 pandemic in the United States. American Politics Research, 49(2), 215–220. [ Google Scholar ]
• Baccini, L. , Brodeur, A. , & Weymouth, S. (2021) The COVID‐19 pandemic and the 2020 U.S. presidential election. Journal of Population Economics, 34, 739–767. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baert, S. , Lippens, L. , Moens, E. , Sterkens, P. , & Weytjens, J. (2020a). How do we think the COVID‐19 crisis will affect our careers (if any remain)? IZA Discussion Paper No. 13164. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13164.pdf [ Google Scholar ]
• Baert, S. , Lippens, L. , Moens, E. , Sterkens, P. , & Weytjens, J. (2020b). The COVID‐19 crisis and telework: A research survey on experiences, expectations and hopes. IZA Discussion Paper No. 13229. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13229.pdf [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baker, S. R. , Bloom, N. , & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. [ Google Scholar ]
• Baker, S. R. , Bloom, N. , Davis, S. J. , & Terry, S. J. (2020). COVID‐induced economic uncertainty. NBER Working Paper No. 26983. National Bureau of Economic Research. Cambridge, MA. [ Google Scholar ]
• Baker, S. R. , Farrokhnia, R. A. , Meyer, S. , Pagel, M. , & Yannelis, C. (2020). How does household spending respond to an epidemic? Consumption during the 2020 COVID‐19 pandemic. The Review of Asset Pricing Studies, 10(4), 834–862. 10.1093/rapstu/raaa009 [ DOI ] [ Google Scholar ]
• Baldwin, R. (2020). Keeping the lights on: Economic medicine for a medical shock. VoxEU.Org . Retrieved from https://voxeu.org/article/how-should-we-think-about-containing-covid-19-economic-crisis
• Baqaee, D. , & Farhi, E. (2020). Supply and demand in disaggregated keynesian economies with an application to the Covid‐19 crisis. NBER Working Paper No. 27152. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Bargain, O. , & Aminjonov, U. (2020). Trust and compliance to public health policies in times of COVID‐19. Journal of Public Economics, 192, 104316. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Barrero, J. M. , Bloom, N. , & Davis, S. J. (2020). COVID‐19 is also a reallocation shock , BPEA Conference Drafts, June 25 2020. Brookings Papers on Economic Activity. Retrieved from https://www.brookings.edu/wp-content/uploads/2020/06/Barrero-et-al-conference-draft.pdf
• Barrios, J. M. , & Hochberg, Y. (forthcoming). Risk perception through the lens of politics in the time of the COVID‐19 pandemic. Journal of Financial Economics. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Barrios, J. M. , Benmelech, E. , Hochberg, Y. V. , Sapienza, P. , & Zingales, L. (2021). Civic capital and social distancing during the COVID‐19 pandemic. Journal of Public Economics, 193, 104310. 10.1016/j.jpubeco.2020.104310 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bartik, A. W. , Bertrand, M. , Cullen, Z. , Glaeser, E. L. , Luca, M. , & Stanton, C. (2020). The impact of COVID‐19 on small business outcomes and expectations. Proceedings of the National Academy of Sciences, 117(30), 17656–17666. 10.1073/pnas.2006991117 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bartik, A. W. , Cullen, Z. B. , Glaeser, E. L. , Luca, M. , & Stanton, C. T. (2020). What jobs are being done at home during the Covid‐19 crisis? Evidence from firm‐level surveys. NBER Working Paper No. 27422. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Bartos, V. , Bauer, M. , Cahlikova, J. , & Chytilová, J. (2020). COVID‐19 crisis fuels hostility against foreigners. IZA Discussion Paper No. 13250. Institute of Labor Economics, Bonn, Germany. [ Google Scholar ]
• Bartscher, A. K. , Seitz, S. , Siegloch, S. , Slotwinski, M. , & Wehrhöfer, N. (2020). Social capital and the spread of Covid‐19: Insights from European countries. IZA Discussion Paper No. 13310. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13310.pdf [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Béland, L.‐P. , Brodeur, A. , & Wright, T. (2020). The short‐term economic consequences of Covid‐19: Exposure to disease, remote work and government response. IZA Discussion Paper No. 13159. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13159.pdf [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Béland, L.‐P. , Brodeur, A. , Mikola, D. , & Wright, T. (2020). The short‐term economic consequences of Covid‐19: Occupation tasks and mental health in Canada. IZA Discussion Paper No. 13254. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13254.pdf [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Berger, D. , Herkenhoff, K. , Huang, C. , & Mongey, S. (forthcoming). Testing and Reopening in an SEIR Model. Journal of Economic Dynamics. 10.1016/j.red.2020.11.003 [ DOI ] [ Google Scholar ]
• Bethune, Z. A. , & Korinek, A. (2020). Covid‐19 infection externalities: Pursuing herd immunity or containment? Covid Economics, Vetted and Real Time Papers, 11, 1. Retrieved from https://cepr.org.uk/sites/default/files/CovidEconomics11.pdf#page=6 [ Google Scholar ]
• Bianchi, F. , Faccini, R. , & Melosi, L. (2020). Monetary and fiscal policies in times of large debt: Unity is strength (Revised May 2020). Federal Reserve Bank of Chicago Research Paper Series . 10.21033/wp-2020-13 [ DOI ]
• Bigio, S. , Zhang, M. , & Zilberman, E. (2020). Transfers vs credit policy: Macroeconomic policy trade‐offs during Covid‐19. NBER Working Paper No. 27118. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Binder, C. (2020). Coronavirus fears and macroeconomic expectations. Review of Economics and Statistics, 102: 721–730. [ Google Scholar ]
• Bogliacino, F. , Codagnone, C. , Montealegre, F. , Folkvord, F. , Gómez, C. E. , Charris, R. A. , Liva, G. , Villanueva, F. L. , & Veltri, G. A. (2020). Negative shocks predict change in cognitive function and preferences: Assessing the negative affect and stress hypothesis in the context of the COVID‐19 pandemic and the lockdown mitigation strategy [Preprint]. SocArXiv. 10.31235/osf.io/qhkf9 [ DOI ] [ PMC free article ] [ PubMed ]
• Bonaccorsi, G. , Pierri, F. , Cinelli, M. , Flori, A. , Galeazzi, A. , Porcelli, F. , Schmidt, A. L. , Valensise, C. M. , Scala, A. , Quattrociocchi, W. , & Pammolli, F. (2020). Economic and social consequences of human mobility restrictions under COVID‐19. Proceedings of the National Academy of Sciences, 117(27), 15530–15535. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bonadio, B. , Huo, Z. , Levchenko, A. A. , & Pandalai‐Nayar, N. (2020). Global supply chains in the pandemic. NBER Working Paper No. 27224. National Bureau of Economic Research, Cambridge, MA. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Borjas, G. J. , & Cassidy, H. (2020). The adverse effect of the COVID‐19 labor market shock on immigrant employment. NBER Working Paper No. 27243. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Brinca, P. , Duarte, J. B. , & Faria e Castro, M. (2020). Measuring labor supply and demand shocks during COVID‐19. Working papers 2020‐011E. Federal Reserve Bank, St. Louis, MO. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Briscese, G. , Lacetera, N. , Macis, M. , & Tonin, M. (2020). Compliance with COVID‐19 social‐distancing measures in Italy: The role of expectations and duration. IZA Discussion Paper No. 13092. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13092.pdf [ Google Scholar ]
• Brodeur, A. , Clark, A. , Fleche, S. , & Powdthavee, N. (2021). COVID‐19, lockdowns and well‐being: Evidence from Google trends. Journal of Public Economics, 193, 104346. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brodeur, A. , Cook, N. , & Wright, T. (2021). On the effects of Covid‐19 safer‐at‐home policies on social distancing, car crashes and pollution. Journal of Environmental Economics and Management, 102427. 10.1016/j.jeem.2021.102427 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brodeur, A. , Grigoryeva, I. , & Kattan, L. (2020). Stay‐at‐home orders, social distancing and trust. IZA Discussion Paper No. 13234. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13234.pdf [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brück, T. , Ferguson, N. , Justino, P. , & Stojetz, W. (2020). Trust in the time of corona. IZA Discussion Paper No. 13386. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13386.pdf [ Google Scholar ]
• Brynjolfsson, E. , Horton, J. J. , Ozimek, A. , Rock, D. , Sharma, G. , & TuYe, H.‐Y. (2020). COVID‐19 and remote work: An early look at US data. NBER Working Paper No. 27344. National Bureau of Economic Research, Cambridge, MA. 10.3386/w27344 [ DOI ] [ Google Scholar ]
• Bui, T. T. M. , Button, P. , & Picciotti, E. G. (2020). Early evidence on the impact of Coronavirus disease 2019 (COVID‐19) and the recession on older workers. Public Policy & Aging Report, 30(4), 154–159. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bursztyn, L. , Rao, A. , Roth, C. P. , & Yanagizawa‐Drott, D. H. (2020). Misinformation during a pandemic. NBER Working Paper No. 27417. National Bureau of Economic Research, Cambridge, MA. Retrieved from https://www.nber.org/papers/w27417.pdf [ Google Scholar ]
• Campello, M. , Kankanhalli, G. , & Muthukrishnan, P. (2020). Corporate hiring under COVID‐19: Labor market concentration, downskilling, and income inequality. NBER Working Paper No. 27208. National Bureau of Economic Research, Cambridge, MA. Retrieved from https://www.nber.org/papers/w27208.pdf [ Google Scholar ]
• Campos‐Mercade, P. , Meier, A. N. , Schneider, F. H. , & Wengström, E. (2021). Prosociality predicts health behaviors during the COVID‐19 pandemic. Journal of Public Economics, 195, 104367. 10.1016/j.jpubeco.2021.104367 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Carlsson‐Szlezak, P. , Reeves, M. , & Swartz, P. (2020a). Understanding the economic shock of Coronavirus. Harvard Business Review . Retrieved from https://hbr.org/2020/03/understanding-the-economic-shock-of-coronavirus
• Carlsson‐Szlezak, P. , Reeves, M. , & Swartz, P. (2020b). What coronavirus could Mea `1n for the global economy. Harvard Business Review . Retrieved from https://hbr.org/2020/03/what-coronavirus-could-mean-for-the-global-economy
• Céspedes, L. F. , Chang, R. , & Velasco, A. (2020). The macroeconomics of a pandemic: A minimalist model. NBER Working Paper No. 27228. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Chang, H. H. , & Meyerhoefer, C. D. (2020). COVID‐19 and the demand for online food shopping services: Empirical evidence from Taiwan. American Journal of Agricultural Economics. 103(2), 448–465. 10.1111/ajae.12170 [ DOI ] [ Google Scholar ]
• Chang, R. , & Velasco, A. (2020). Economic policy incentives to preserve lives and livelihoods. NBER Working Paper No. 27020. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Chatterji, P. , & Li, Y. (2021). Effects of the COVID‐19 Pandemic on outpatient providers in the US. Medical Care. 59(1), 58–61. 10.1097/MLR.0000000000001448 [ DOI ] [ PubMed ] [ Google Scholar ]
• Cheng, C. , Barceló, J. , Hartnett, A. S. , Kubinec, R. , & Messerschmidt, L. (2020). COVID‐19 government response event dataset (CoronaNet v.1.0). Nature Human Behaviour, 4(7), 756–768. [ DOI ] [ PubMed ] [ Google Scholar ]
• Cheng, W. , Carlin, P. , Carroll, J. , Gupta, S. , Rojas, F. L. , Montenovo, L. , Nguyen, T. D. , Schmutte, I. M. , Scrivner, O. , Simon, K. I. , Wing, C. , & Weinberg, B. (2020). Back to business and (re)employing workers? Labor market activity during state COVID‐19 reopenings. NBER Working Paper No. 27419. National Bureau of Economic Research, Cambridge, MA.
• Chetty, R. , Friedman, J. N. , Hendren, N. , Stepner, M. , & Team, T. O. I. (2020). How did COVID‐19 and stabilization policies affect spending and employment? A new real‐time economic tracker based on private sector data. NBER Working Paper No. 27431. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Chiou, L. , & Tucker, C. (2020). Social distancing, internet access and inequality. NBER Working Paper No. 26982. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Cicala, S. , Holland, S. P. , Mansur, E. T. , Muller, N. Z. , & Yates, A. J. (2020). Expected health effects of reduced air pollution from COVID‐19 social distancing. NBER Working Paper No. 27135. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Cleevely, M. , Susskind, D. , Vines, D. , Vines, L. , & Wills, S. (2020). A workable strategy for COVID‐19 testing: Stratified periodic testing rather than universal random testing. Oxford Review of Economic Policy, 36(Supplement_1), S14–S37. [ Google Scholar ]
• Clemens, J. , & Veuger, S. (2020). Implications of the Covid‐19 Pandemic for State Government Tax Revenues. National Tax Journal. 73(3), 619–644. 10.17310/ntj.2020.3.01 [ DOI ] [ Google Scholar ]
• Coelho, F. C. , Lana, R. M. , Cruz, O. G. , Villela, D. A. M. , Bastos, L. S. , Piontti, A. P. y , Davis, J. T. , Vespignani, A. , Codeço, C. T. , & Gomes, M. F. C. (2020). Assessing the spread of COVID‐19 in Brazil: Mobility, morbidity and social vulnerability. Plos One, 15(9), e0238214. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Codagnone, C. , Bogliacino, F. , Gómez, C. , Charris, R. , Montealegre, F. , Liva, G. , Lupiáñez‐Villanueva, F. , Folkvord, F. , & Veltri, G. A. (2020). Assessing concerns for the economic consequence of the COVID‐19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom. Plos One, 15(10), e0240876. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Codagnone, C. , Bogliacino, F. , Gómez, C. E. , Folkvord, F. , Liva, G. , Charris, R. A. , Montealegre, F. , Lupiáñez‐Villanueva, F. , & Veltri, G. A. (2020). Restarting “normal” life after Covid‐19 and the lockdown: Evidence from Spain, the United Kingdom, and Italy [Preprint]. SocArXiv. 10.31235/osf.io/vd4cq [ DOI ] [ PMC free article ] [ PubMed ]
• Coibion, O. , Gorodnichenko, Y. , & Weber, M. (2020). The cost of the Covid‐19 crisis: Lockdowns, macroeconomic expectations, and consumer spending. NBER Working Paper No. 27141. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Coibion, O. , Gorodnichenko, Y. , & Weber, M. (2020b). Labor markets during the COVID‐19 crisis: A preliminary view. NBER Working Paper No. 27017. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Couch, K. A. , Fairlie, R. W. , & Xu, H. (2020a). Early evidence of the impacts of COVID‐19 on minority unemployment. Journal of Public Economics, 192, 104287. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Couch, K. A. , Fairlie, R. W. , & Xu, H. (2020b). Gender and the COVID‐19 labor market downturn. stanford institute for economic policy research. Working Paper No. 20–037. [ Google Scholar ]
• Coven, J. , & Gupta, A. (2020). Disparities in mobility responses to COVID‐19. NYU Stern Working Paper, 2020. Retrieved from https://static1.squarespace.com/static/56086d00e4b0fb7874bc2d42/t/5ebf201183c6f016ca3abd91/1589583893816/DemographicCovid.pdf [ Google Scholar ]
• Cui, Z. , Heal, G. , & Kunreuther, H. (2020). Covid‐19, shelter‐in place strategies and tipping. NBER Working Paper No. 27124. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Daniele, G. , Martinangeli, A. F. M. , Passarelli, F. , Sas, W. , & Windsteiger, L. (2020). Wind of change? Experimental survey evidence on the Covid‐19 shock and socio‐political attitudes in Europe. CESifo Working Paper No. 8517. Center for Economic Studies and Ifo Institute (CESifo). Retrieved from https://www.econstor.eu/handle/10419/223589 [ Google Scholar ]
• Dave, D. , Friedson, A. , Matsuzawa, K. , Sabia, J. J. , & Safford, S. (2020a). Were urban cowboys enough to control COVID‐19? Local shelter‐in‐place orders and coronavirus case growth. Journal of Urban Economics, 103294. 10.1016/j.jue.2020.103294 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dave, D. M. , Friedson, A. I. , Matsuzawa, K. , McNichols, D. , & Sabia, J. J. (2020). Did the Wisconsin Supreme court restart a COVID‐19 epidemic? Evidence from a natural experiment. NBER Working Paper No. 27322. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Davillas, A. , & Jones, A. M. (2020). The COVID‐19 pandemic and its impact on inequality of opportunity in psychological distress in the UK. ISER Working Paper Series No. 2020–07. Institute for Social and Economic Research. Retrieved from https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2020-07.pdf [ Google Scholar ]
• de Pedraza, P. , Guzi, M. , & Tijdens, K. (2020). Life dissatisfaction and anxiety in COVID‐19 pandemic . EUR 30243 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978‐92‐76‐19341‐8. 10.2760/755327 [ DOI ]
• Demirguc‐Kunt, A. , Lokshin, M. M. , & Torre, I. (2020). The sooner, the better: The early economic impact of non‐pharmaceutical interventions during the COVID‐19 pandemic. Policy Research Working Paper No. 9257. The World Bank, Washington, DC. Retrieved from http://documents.worldbank.org/curated/en/636851590495700748/The-Sooner-the-Better-The-Early-Economic-Impact-of-Non-Pharmaceutical-Interventions-during-the-COVID-19-Pandemic [ Google Scholar ]
• Didier, T. , Huneeus, F. , Larrain, M. , & Schmukler, S. L. (2020). Financing firms in hibernation during the COVID‐19 pandemic. Policy Research Working Paper No. 9236. The World Bank, Washington, DC. Retrieved from http://documents.worldbank.org/curated/en/818801588952012929/Financing-Firms-in-Hibernation-during-the-COVID-19-Pandemic [ Google Scholar ]
• Dingel, J. I. , & Neiman, B. (2020). How many jobs can be done at home? Journal of Public Economics, 189, 104235. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dong, E. , Du, H. , & Gardner, L. (2020). An interactive web‐based dashboard to track COVID‐19 in real time Lancet. Infectious Diseases, 20(5), 533–534. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Durante, R. , Guiso, L. , & Gulino, G. (2021). Asocial capital: Civic culture and social distancing during COVID‐19. Journal of Public Economics, 194, 104342. 10.1016/j.jpubeco.2020.104342 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eberhardt, J. N. , Breuckmann, N. P. , & Eberhardt, C. S. (2020). Multi‐stage group testing improves efficiency of large‐scale COVID‐19 screening. Journal of Clinical Virology, 104382. 10.1016/j.jcv.2020.104382 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Egorov, G. , Enikolopov, R. , Makarin, A. , & Petrova, M. (2021). Divided we stay home: Social distancing and ethnic diversity. Journal of Public Economics, 194, 104328. 10.1016/j.jpubeco.2020.104328 [ DOI ] [ Google Scholar ]
• Eichenbaum, M. S. , Rebelo, S. , & Trabandt, M. (2020). The macroeconomics of epidemics NBER Working Paper No. 26882. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Elenev, V. , Landvoigt, T. , & Van Nieuwerburgh, S. (2020). Can the Covid bailouts save the economy? NBER Working Paper No. 27207. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Elgin, C. , Basbug, G. , & Yalaman, A. (2020). Economic policy responses to a pandemic: Developing the Covid‐19 economic stimulus index. COVID Economics, Vetted and Real‐Time Papers, 3, 40–53. [ Google Scholar ]
• Eppinger, P. S. , Felbermayr, G. , Krebs, O. , & Kukharskyy, B. (2020). Covid‐19 shocking global value chains. Kiel Working Paper No. 2167. Kiel Institute for the World Economy, Germany. Retrieved from https://www.econstor.eu/handle/10419/224061 [ Google Scholar ]
• Etheridge, B. , & Spantig, L. (2020). The gender gap in mental well‐being during the Covid‐19 outbreak: Evidence from the UK. ISER Working Paper Series No. 2020–08. Institute for Social and Economic Research. Retrieved from https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2020-08.pdf [ Google Scholar ]
• Fairlie, R. (2020). The impact of COVID‐19 on small business owners: Evidence from the first three months after widespread social‐distancing restrictions. Journal of Economics & Management Strategy, 29(4), 727–740. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fan, Y. , Orhun, A. Y. , & Turjeman, D. (2020). Heterogeneous actions, beliefs, constraints and risk tolerance during the COVID‐19 pandemic. NBER Working Paper No. 27211. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Fang, H. , Wang, L. , & Yang, Y. (2020). Human mobility restrictions and the spread of the novel coronavirus (2019‐nCov) in China. Journal of Public Economics, 191, 104272. 10.1016/j.jpubeco.2020.104272 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Farboodi, M. , Jarosch, G. , & Shimer, R. (2020). Internal and external effects of social distancing in a pandemic. NBER Working Paper No. 27059. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Farré, L. , Fawaz, Y. , González, L. , & Graves, J. (2020). How the COVID‐19 lockdown affected gender inequality in paid and unpaid work in Spain. IZA Discussion Paper No. 13434. Institute of Labor Economics, Bonn, Germany. Retrieved from http://ftp.iza.org/dp13434.pdf [ Google Scholar ]
• Ferguson, N. , Laydon, D. , Nedjati Gilani, G. , Imai, N. , Ainslie, K. , Baguelin, M. , Bhatia, S. , Boonyasiri, A. , Cucunuba Perez, Z. , Cuomo‐Dannenburg, G. , Dighe, A. , Dorigatti, I. , Fu, H. , Gaythorpe, K. , Green, W. , Hamlet, A. , Hinsley, W. , Okell, L. , Van Elsland, S. , …, & Ghani, A. (2020). Impact of non‐pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College London. [ Google Scholar ]
• Fetzer, T. R. , Witte, M. , Hensel, L. , Jachimowicz, J. , Haushofer, J. , Ivchenko, A. , Caria, S. , Reutskaja, E. , Roth, C. P. , Fiorin, S. , Gómez, M. , Kraft‐Todd, G. , Götz, F. M. , & Yoeli, E. (2020). Global behaviors and perceptions at the onset of the COVID‐19 pandemic. NBER Working Paper No. 27082. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Fezzi, C. , & Fanghella, V. (2020). Real‐time estimation of the short‐run impact of COVID‐19 on economic activity using electricity market data. Environmental and Resource Economics, 76(4), 885–900. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fong, M. W. , Gao, H. , Wong, J. Y. , Xiao, J. , Shiu, E. Y. C. , Ryu, S. , & Cowling, B. J. (2020). Nonpharmaceutical measures for pandemic influenza in nonhealthcare settings—Social distancing measures. Emerging Infectious Diseases, 26(5), 976–984. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fornaro, L. , & Wolf, M. (2020). Covid‐19 coronavirus and macroeconomic policy. Barcelona GSE Working Paper No. 1168. Barcelona Graduate School of Economics, Barcelona, Spain. Retrieved from https://fondazionecerm.it/wp-content/uploads/2020/04/GSE-Covid-19-Coronavirus-and-Macroeconomic-Policy.pdf [ Google Scholar ]
• Forsythe, E. , Kahn, L. B. , Lange, F. , & Wiczer, D. (2020). Labor demand in the time of COVID‐19: Evidence from vacancy postings and UI claims. Journal of Public Economics, 189, 104238. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friedson, A. I. , McNichols, D. , Sabia, J. J. , & Dave, D. (2020). Did California's shelter‐in‐place order work? Early Coronavirus‐related public health effects. NBER Working Paper No. 26992. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Galasso, V. , Pons, V. , Profeta, P. , Becher, M. , Brouard, S. , & Foucault, M. (2020). Gender differences in COVID‐19 attitudes and behavior: Panel evidence from eight countries. Proceedings of the National Academy of Sciences, 117(44), 27285. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Galeazzi, A. , Cinelli, M. , Bonaccorsi, G. , Pierri, F. , Schmidt, A. L. , Scala, A. , Pammolli, F. , & Quattrociocchi, W. (2020). Human mobility in response to COVID‐19 in France, Italy and UK. ArXiv:2005.06341 [Physics] . Retrieved from http://arxiv.org/abs/2005.06341 [ DOI ] [ PMC free article ] [ PubMed ]
• Goldstein, J. R. , & Lee, R. D. (2020). Demographic perspectives on the mortality of COVID‐19 and other epidemics. Proceedings of the National Academy of Sciences, 117(36), 22035. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gollier, C. , & Gossner, O. ((2020). Group testing against Covid‐19 . Retrieved from http://eprints.lse.ac.uk/104228/3/covid_economics.pdf
• Gonzalez‐Eiras, M. , & Niepelt, D. (2020). On the optimal “Lockdown” during an epidemic. Working Paper No. 20.01. Swiss National Bank Study Center Gerzensee. Retrieved from https://szgerzensee.ch/fileadmin/Dateien_Anwender/Dokumente/working_papers/wp-2001.pdf [ Google Scholar ]
• Gourinchas, P.‐O. (2020). Flattening the pandemic and recession curves. Mitigating the COVID Economic Crisis: Act Fast and Do Whatever. Retrieved from http://viet-studies.net/kinhte/COVIDEconomicCrisis.pdf#page=38
• Greenstone, M. , & Nigam, V. (2020). Does social distancing matter?. University of Chicago, Becker Friedman Institute for Economics; Working Paper No. 2020–26. Retrieved from http://iepecdg.com.br/wp-content/uploads/2020/04/SSRN-id3561244.pdf [ Google Scholar ]
• Gregory, V. , Menzio, G. , & Wiczer, D. G. (2020). Pandemic recession: L or V‐shaped? Quarterly Review, 40(1). 10.21034/qr.4011 [ DOI ] [ Google Scholar ]
• Guerrieri, V. , Lorenzoni, G. , Straub, L. , & Werning, I. (2020). Macroeconomic implications of COVID‐19: Can negative supply shocks cause demand shortages? NBER Working Paper No. 26918. National Bureau of Economic Research, Cambridge, MA. 10.3386/w26918 [ DOI ] [ Google Scholar ]
• Gupta, S. , Montenovo, L. , Nguyen, T. D. , Rojas, F. L. , Schmutte, I. M. , Simon, K. I. , Weinberg, B. A. , & Wing, C. (2020). Effects of social distancing policy on labor market outcomes. NBER Working Paper No. 27280. National Bureau of Economic Research, Cambridge, MA. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hamermesh, D. S. (2020). Life satisfaction, loneliness and togetherness, with an application to Covid‐19 lock‐downs. Review of Economics of the Household, 18: 983–1000. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Harris, J. E. (2020). Reopening Under COVID‐19: What to Watch For. NBER Working Paper No. 27166. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Hartl, T. , Wälde, K. , & Weber, E. (2020). Measuring the impact of the German public shutdown on the spread of Covid‐19. Center for Economic Policy Research, 1, 25–42. [ Google Scholar ]
• Hassan, T. A. , Hollander, S. , van Lent, L. , & Tahoun, A. (2020). Firm‐level exposure to epidemic diseases: Covid‐19, SARS, and H1N1. NBER Working Paper No. 26971. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• He, G. , Pan, Y. , & Tanaka, T. (2020). COVID‐19, city lockdowns, and air pollution: Evidence from China. HKUST IEMS Working Paper No. 2019‐72, HongKong.
• Hermosilla, M. , Ni, J. , Wang, H. , & Zhang, J. (2020). Unmet needs: Healthcare crowd‐out during the COVID‐19 pandemic. Retrieved from https://ssrn.com/abstract=3607594 or 10.2139/ssrn.3607594 [ DOI ]
• Hsiang, S. , Allen, D. , Annan‐Phan, S. , Bell, K. , Bolliger, I. , Chong, T. , Druckenmiller, H. , Huang, L. Y. , Hultgren, A. , Krasovich, E. , Lau, P. , Lee, J. , Rolf, E. , Tseng, J. , & Wu, T. (2020). The effect of large‐scale anti‐contagion policies on the COVID‐19 pandemic. Nature, 584(7820), 262–267. [ DOI ] [ PubMed ] [ Google Scholar ]
• International Monetary Fund . (2020). World economic outlook update, October 2020: A long and difficult ascent. IMF. Retrieved from https://www.imf.org/en/Publications/WEO/Issues/2020/09/30/world-economic-outlook-october-2020
• Jinjarak, Y. , Ahmed, R. , Nair‐Desai, S. , Xin, W. , & Aizenman, J. (2020). Accounting for global COVID‐19 diffusion patterns, January–April 2020. Economics of Disasters and Climate Change, 4(3), 515–559. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• John Hopkins University . (2020). New cases of COVID‐19 in world countries. Johns Hopkins Coronavirus Resource Center. Retrieved from https://coronavirus.jhu.edu/data/new-cases [ Google Scholar ]
• Jones, C. J. , Philippon, T. , & Venkateswaran, V. (2020). Optimal mitigation policies in a pandemic: Social distancing and working from home. NBER Working Paper No. 26984. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Jordà, Ò. , Singh, S. R. , & Taylor, A. M. (2020). Longer‐run economic consequences of pandemics. NBER Working Paper No. 26934. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Juranek, S. , & Zoutman, F. (2020). The effect of social distancing measures on the demand for intensive care: Evidence on COVID‐19 in Scandinavia. NHH Discussion Paper 02/2020. Retrieved from https://openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2652920/0220.pdf?sequence=1&isAllowed=y
• Kahn, L. B. , Lange, F. , & Wiczer, D. G. (2020). Labor demand in the time of COVID‐19: Evidence from vacancy postings and UI claims. NBER Working Paper No. 27061. National Bureau of Economic Research, Cambridge, MA. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kapteyn, A. , Angrisani, M. , Bennett, D. , Bruin, W. B. de, Darling, J. , Gutsche, T. , Liu, Y. , Meijer, E. , Perez‐Arce, F. , Schaner, S. , Thomas, K. , & Weerman, B. (2020). Tracking the effect of the COVID‐19 pandemic on the lives of American households. Survey Research Methods, 14(2), 179–186. [ Google Scholar ]
• Knittel, C. R. , & Ozaltun, B. (2020). What does and does not correlate with COVID‐19 death rates. NBER Working Paper No. 27391. National Bureau of Economic Research, Cambridge, MA. Retrieved from http://www.nber.org/papers/w27391 [ Google Scholar ]
• Kopecky, K. A. , & Zha, T. (2020). Impacts of COVID‐19: Mitigation efforts versus herd immunity. Policy Hub No. 03–2020. Federal Reserve Bank of Atlanta. Retrieved from https://www.frbatlanta.org/-/media/documents/research/publications/policy-hub/2020/04/17/impacts-of-covid-19-mitigation-efforts-versus-herd-immunity.pdf [ Google Scholar ]
• Kozlowski, J. , Veldkamp, L. , & Venkateswaran, V. (2020). Scarring body and mind: The long‐term belief‐scarring effects of COVID‐19. NBER Working Paper No. 27439. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Kraemer, M. U. G. , Yang, C.‐H. , Gutierrez, B. , Wu, C.‐H. , Klein, B. , Pigott, D. M. , Group, O. C.‐19 D. W. , Plessis, L. d. , Faria, N. R. , Li, R. , Hanage, W. P. , Brownstein, J. S. , Layan, M. , Vespignani, A. , Tian, H. , Dye, C. , Pybus, O. G. , & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID‐19 epidemic in China. Science, 368(6490), 493–497. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kurmann, A. , Lale, E. , & Ta, L. (2020). The impact of COVID‐19 on US employment and hours: Real‐time estimates with homebase data. Retreived from http://www.andrekurmann.com/hb_covid .
• Lewis, D. , Mertens, K. , & Stock, J. H. (2020). U.S. economic activity during the early weeks of the SARS‐Cov‐2 outbreak. NBER Working Paper No. 26954. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Lin, P. Z. , & Meissner, C. M. (2020). A note on long‐run persistence of public health outcomes in pandemics. NBER Working Paper No. 27119. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Lomas, N . (2020). Google is now publishing coronavirus mobility reports, feeding off users’ location history. TechCrunch. Retrieved from https://social.techcrunch.com/2020/04/03/google-is-now-publishing-coronavirus-mobility-reports-feeding-off-users-location-history/ [ Google Scholar ]
• Lu, H. , Nie, P. , & Qian, L. (2020). Do quarantine experiences and attitudes towards COVID‐19 affect the distribution of mental health in China? A quantile regression analysis. Applied Research in Quality of Life. 10.1007/s11482-020-09851-0 [ DOI ] [ PMC free article ] [ PubMed ]
• Ludvigson, S. C. , Ma, S. , & Ng, S. (2020). Covid19 and the macroeconomic effects of costly disasters. NBER Working Paper No. 26987. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Makris, M. (2020). Covid and social distancing with a heterogenous population. School of Economics Discussion Paper No. 2002. University of Kent. Retrieved from https://www.kent.ac.uk/economics/repec/2002.pdf [ Google Scholar ]
• Maloney, W. F. , & Taskin, T. (2020). Determinants of social distancing and economic activity during COVID‐19: A global view (No. WPS9242; pp. 1–23). The World Bank. Retrieved from http://documents.worldbank.org/curated/en/325021589288466494/Determinants-of-Social-Distancing-and-Economic-Activity-during-COVID-19-A-Global-View [ Google Scholar ]
• Mandavilli, A. (2020). Wondering about social distancing? The New York Times . Retrieved from https://www.nytimes.com/2020/03/16/smarter-living/coronavirus-social-distancing.html
• Martín‐Calvo, D. , Aleta, A. , Pentland, A. , Moreno, Y. , & Moro, E. (2020). Effectiveness of social distancing strategies for protecting a community from a pandemic with a data‐driven contact network based on census and real‐world mobility data. MIT Connection Science . Retrieved from https://connection.mit.edu/sites/default/files/publication-pdfs/Preliminary_Report_Effectiveness_of_social_distance_strategies_COVID-19%20%281%29.pdf
• McKibbin, W. J. , & Fernando, R. (2020). The global macroeconomic impacts of COVID‐19: seven scenarios. CAMA Working Paper No. 19/2020. The Center for Applied Macroeconomic Analysis, Australian National University. Retrieved from https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2020-03/19_2020_mckibbin_fernando_0.pdf [ Google Scholar ]
• McLaren, J. (2020). Racial disparity in COVID‐19 deaths: Seeking economic roots with census data. NBER Working Paper No. 27407. National Bureau of Economic Research, Cambridge, MA. Retrieved from http://www.nber.org/papers/w27407 [ Google Scholar ]
• Montenovo, L. , Jiang, X. , Rojas, F. L. , Schmutte, I. M. , Simon, K. I. , Weinberg, B. A. , & Wing, C. (2020). Determinants of disparities in Covid‐19 job losses. NBER working paper No. 27132. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Mulligan, C. B. (2020). Economic activity and the value of medical innovation during a pandemic. NBER Working Paper No. 27060. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Murray, G. R. , & Murray, S. M. (2020). Following doctors’ advice: explaining the issuance of stay‐at‐home orders related to the coronavirus disease 2019 (COVID‐19) by U.S. governors (OSF Preprints No. 92ay6). Center for Open Science. 10.31219/osf.io/92ay6 [ DOI ]
• Nguyen, T. D. , Gupta, S. , Andersen, M. , Bento, A. , Simon, K. I. , & Wing, C. (2020). Impacts of state reopening policy on human mobility. NBER Working Paper No. 27235. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• OECD . (2020). OECD Economic Outlook, Volume 2020 Issue 1: Preliminary version | OECD iLibrary . Retrieved from https://www.oecd-ilibrary.org/sites/0d1d1e2e-en/index.html?itemId=/content/publication/0d1d1e2e-en
• Oliver, N. , Letouzé, E. , Sterly, H. , Delataille, S. , De Nadai, M. , Lepri, B. , Lambiotte, R. , Benjamins, R. , Cattuto, C. , Colizza, V. , de Cordes, N. , Fraiberger, S. P. , Koebe, T. , Lehmann, S. , Murillo, J. , Pentland, A. , Pham, P. N. , Pivetta, F. , Salah, A. A. , & Vinck, P. (2020). Mobile phone data and COVID‐19: Missing an opportunity? ArXiv:2003.12347 . Retrieved from https://arxiv.org/ftp/arxiv/papers/2003/2003.12347.pdf [ DOI ] [ PMC free article ] [ PubMed ]
• Oswald, A. J. , & Powdthavee, N. (2020). The case for releasing the young from lockdown: A briefing paper for policymakers. IZA Discussion Papers No. 13113. Institute of Labor Economics. Retrieved from http://ftp.iza.org/dp13113.pdf [ Google Scholar ]
• Papageorge, N. W. , Zahn, M. V. , Belot, M. , van den Broek‐Altenburg, E. , Choi, S. , Jamison, J. C. , & Tripodi, E. (2020). Socio‐demographic factors associated with self‐protecting behavior during the Covid‐19 pandemic. Journal of Population Economics, 34, 691–738. 10.1007/s00148-020-00818-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Papanikolaou, D. , & Schmidt, L. D. W. (2020). Working remotely and the supply‐side impact of Covid‐19. NBER Working Paper No. 27330. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Porcher, S. (2020). “Contagion”: The determinants of governments’ public health responses to COVID‐19 all around the world . HAL Archives. Retrieved from https://halshs.archives-ouvertes.fr/halshs-02567286/document
• Qiu, Y. , Chen, X. , & Shi, W. (2020). Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID‐19) in China. Journal of Population Economics, 1–46. 10.1007/s00148-020-00778-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rampini, A. A. (2020). Sequential lifting of COVID‐19 interventions with population heterogeneity. NBER Working Paper No. 27063. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Rojas, F. L. , Jiang, X. , Montenovo, L. , Simon, K. I. , Weinberg, B. A. , & Wing, C. (2020). Is the cure worse than the problem itself? Immediate labor market effects of COVID‐19 case rates and school closures in the U.S. NBER working paper No. 27127. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Roser, M. , Ritchie, H. , Ortiz‐Ospina, E. , & Hasell, J. (2020). Coronavirus pandemic (COVID‐19). Our World in Data . Retrieved from https://ourworldindata.org/coronavirus
• Schild, L. , Ling, C. , Blackburn, J. , Stringhini, G. , Zhang, Y. , & Zannettou, S. (2020). “Go eat a bat, Chang!”: An early look on the emergence of sinophobic behavior on web communities in the face of COVID‐19. ArXiv:2004.04046 . Retrieved from http://arxiv.org/abs/2004.04046
• Silverman, J. D. , Hupert, N. , & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state‐specific prevalence of SARS‐CoV‐2 in the United States. Science Translational Medicine. 10.1126/scitranslmed.abc1126 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Simonov, A. , Sacher, S. K. , Dubé, J.‐P. H. , & Biswas, S. (2020). The persuasive effect of Fox news: Non‐compliance with social distancing during the Covid‐19 pandemic. NBER Working Paper No. 27237. National Bureau of Economic Research, Cambridge, MA. [ Google Scholar ]
• Taipale, J. , Romer, P. , & Linnarsson, S. (2020). Population‐scale testing can suppress the spread of COVID‐19 . MedRxiv, 2020.04.27.20078329. 10.1101/2020.04.27.20078329 [ DOI ]
• Tubadji, A. , Boy, F. , & Webber, D. (2020). Narrative economics, public policy and mental health. Center for Economic Policy Research, 20, 109–131. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Weder di Mauro, B. (2020). Macroeconomics of the flu. Center for Economic Policy Research. Retrieved from http://repository.graduateinstitute.ch/record/298218 [ Google Scholar ]
• Wells, C. R. , Townsend, J. P. , Pandey, A. , Krieger, G. , Singer, B. , McDonald, R. H. , Moghadas, S. M. , & Galvani, A. P. (2020). Optimal COVID‐19 quarantine and testing strategies . MedRxiv, 2020.10.27.20211631. 10.1101/2020.10.27.20211631 [ DOI ] [ PMC free article ] [ PubMed ]
• World Bank . (2020). Global Economic Prospects. Washington, DC: World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/33748 [ Google Scholar ]
• Yasenov, V. I. (2020). Who can work from home? IZA Discussion Paper No. 13197. Institute of Labor Economics, Cambridge, MA. Retrieved from http://ftp.iza.org/dp13197.pdf [ Google Scholar ]

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