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Mortality analyses, mortality in the most affected countries.

For the twenty countries currently most affected by COVID-19 worldwide, the bars in the chart below show the number of deaths either per 100 confirmed cases (observed case-fatality ratio) or per 100,000 population (this represents a country’s general population, with both confirmed cases and healthy people). Countries at the top of this figure have the most deaths proportionally to their COVID-19 cases or population, not necessarily the most deaths overall.

Worldwide mortality

The diagonal lines on the chart below correspond to different case fatality ratios (the number of deaths divided by the number of confirmed cases). Countries falling on the uppermost lines have the highest observed case fatality ratios. Points with a black border correspond to the 20 most affected countries by COVID-19 worldwide, based on the number of deaths. Hover over the circles to see the country name and a ratio value. Use the boxes on the top to toggle between: 1) mortality per absolute number of cases (total confirmed cases within a country); and mortality per 100,000 people (this represents a country’s general population, with both confirmed cases and healthy people).

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How does mortality differ across countries.

One of the most important ways to measure the burden of COVID-19 is mortality. Countries throughout the world have reported very different case fatality ratios – the number of deaths divided by the number of confirmed cases. Differences in mortality numbers can be caused by:

• Differences in the number of people tested: With more testing, more people with milder cases are identified. This lowers the case-fatality ratio.
• Demographics: For example, mortality tends to be higher in older populations.
• Characteristics of the healthcare system: For example, mortality may rise as hospitals become overwhelmed and have fewer resources.
• Other factors, many of which remain unknown.

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COVID-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide

Wake-up call to all countries to step up mental health services and support.

In the first year of the COVID-19 pandemic, global prevalence of anxiety and depression increased by a massive 25%, according to a scientific brief released by the World Health Organization (WHO) today. The brief also highlights who has been most affected and summarizes the effect of the pandemic on the availability of mental health services and how this has changed during the pandemic.

Concerns about potential increases in mental health conditions had already prompted 90% of countries surveyed to include mental health and psychosocial support in their COVID-19 response plans, but major gaps and concerns remain.

“The information we have now about the impact of COVID-19 on the world’s mental health is just the tip of the iceberg,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This is a wake-up call to all countries to pay more attention to mental health and do a better job of supporting their populations’ mental health.”

Multiple stress factors

One major explanation for the increase is the unprecedented stress caused by the social isolation resulting from the pandemic. Linked to this were constraints on people’s ability to work, seek support from loved ones and engage in their communities.

Loneliness, fear of infection, suffering and death for oneself and for loved ones, grief after bereavement and financial worries have also all been cited as stressors leading to anxiety and depression. Among health workers, exhaustion has been a major trigger for suicidal thinking.

Young people and women worst hit

The brief, which is informed by a comprehensive review of existing evidence about the impact of COVID-19 on mental health and mental health services, and includes estimates from the latest Global Burden of Disease study, shows that the pandemic has affected the mental health of young people and that they are disproportionally at risk of suicidal and self-harming behaviours. It also indicates that women have been more severely impacted than men and that people with pre-existing physical health conditions, such as asthma, cancer and heart disease, were more likely to develop symptoms of mental disorders.

Data suggests that people with pre-existing mental disorders do not appear to be disproportionately vulnerable to COVID-19 infection. Yet, when these people do become infected, they are more likely to suffer hospitalization, severe illness and death compared with people without mental disorders. People with more severe mental disorders, such as psychoses, and young people with mental disorders, are particularly at risk.

Gaps in care

This increase in the prevalence of mental health problems has coincided with severe disruptions to mental health services, leaving huge gaps in care for those who need it most. For much of the pandemic, services for mental, neurological and substance use conditions were the most disrupted among all essential health services reported by WHO Member States. Many countries also reported major disruptions in life-saving services for mental health, including for suicide prevention.

By the end of 2021 the situation had somewhat improved but today too many people remain unable to get the care and support they need for both pre-existing and newly developed mental health conditions.

Unable to access face-to-face care, many people have sought support online, signaling an urgent need to make reliable and effective digital tools available and easily accessible. However, developing and deploying digital interventions remains a major challenge in resource-limited countries and settings.

WHO and country action

Since the early days of the pandemic, WHO and partners have worked to develop and disseminate resources in multiple languages and formats to help different groups cope with and respond to the mental health impacts of COVID-19. For example, WHO produced a story book for 6-11-year-olds, My Hero is You, now available in 142 languages and 61 multimedia adaptations, as well as a toolkit for supporting older adults available in 16 languages.

At the same time, the Organization has worked with partners, including other United Nations agencies, international nongovernmental organizations and the Red Cross and Red Crescent Societies, to lead an interagency mental health and psychosocial response to COVID-19. Throughout the pandemic, WHO  has also worked to promote the integration of mental health and psychosocial support across and within all aspects of the global response. 

WHO Member States have recognized the impact of COVID-19 on mental health and are taking action. WHO’s most recent pulse survey on continuity of essential health services indicated that 90% of countries are working to provide mental health and psychosocial support to COVID-19 patients and responders alike. Moreover, at last year’s World Health Assembly, countries emphasized the need to develop and strengthen mental health and psychosocial support services as part of strengthening preparedness, response and resilience to COVID-19 and future public health emergencies. They adopted the updated Comprehensive Mental Health Action Plan 2013-2030, which includes an indicator on preparedness for mental health and psychosocial support in public health emergencies.

Step up investment

However, this commitment to mental health needs to be accompanied by a global step up in investment. Unfortunately, the situation underscores a chronic global shortage of mental health resources that continues today. WHO’s most recent Mental Health Atlas showed that in 2020, governments worldwide spent on average just over 2% of their health budgets on mental health and many low-income countries reported having fewer than 1 mental health worker per 100 000 people.

Dévora Kestel, Director of the Department of Mental Health and Substance Use at WHO, sums up the situation: ”While the pandemic has generated interest in and concern for mental health, it has also revealed historical under-investment in mental health services. Countries must act urgently to ensure that mental health support is available to all.”

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The Changing Political Geography of COVID-19 Over the Last Two Years

Over the past two years, the official count of coronavirus deaths in the United States has risen and is now approaching 1 million lives. Large majorities of Americans say they personally know someone who has been hospitalized or died of the coronavirus , and it has impacted – in varying degrees – nearly every aspect of life .

A new Pew Research Center analysis of official reports of COVID-19-related deaths across the country, based on mortality data collected by The New York Times, shows how the dynamics of the pandemic have shifted over the past two years.

A timeline of the shifting geography of the pandemic

Pew Research Center conducted this analysis to understand how the geography of the coronavirus outbreak has changed over its course. For this analysis, we relied on official reports of deaths attributed to the novel coronavirus collected and maintained by The New York Times .

The estimates provided in this report are subject to several sources of error. There may be significant differences between the true number of deaths due to COVID-19 and the official reported counts of those deaths. There may also be variation across the states in the quality and types of data reported. For example, most states report deaths based on the residency of the deceased person rather than the location where they died. The New York Times collects data from many different local health agencies, and this likely leads to some additional measurement error.

This analysis relies on county-level data. Counties in the United States vary widely in their population sizes, so in many places in the essay, we divide counties into approximately equal-sized groups (in terms of their population) for comparability or report on population adjusted death rates rather than total counts of deaths.

The pandemic has rolled across the U.S. unevenly and in waves. Today, the death toll of the pandemic looks very different from how it looked in the early part of 2020 . The first wave (roughly the first 125,000 deaths from March 2020 through June 2020) was largely geographically concentrated in the Northeast and in particular the New York City region. During the summer of 2020, the largest share of the roughly 80,000 deaths that occurred during the pandemic’s second wave were in the southern parts of the country.

The fall and winter months of 2020 and early 2021 were the deadliest of the pandemic to date. More than 370,000 Americans died of COVID-19 between October 2020 and April 2021; the geographic distinctions that characterized the earlier waves became much less pronounced.

By the spring and summer of 2021, the nationwide death rate had slowed significantly, and vaccines were widely available to all adults who wanted them. But starting at the end of the summer, the fourth and fifth waves (marked by new variants of the virus, delta and then omicron) came in quick succession and claimed more than 300,000 lives.

In many cases, the characteristics of communities that were associated with higher death rates at the beginning of the pandemic are now associated with lower death rates (and vice versa). Early in the pandemic, urban areas were disproportionately impacted. During the first wave, the coronavirus death rate in the 10% of the country that lives in the most densely populated counties was more than nine times that of the death rate among the 10% of the population living in the least densely populated counties. In each subsequent wave, however, the nation’s least dense counties have registered higher death rates than the most densely populated places.

Despite the staggering death toll in densely populated urban areas during the first months of the pandemic (an average 36 monthly deaths per 100,000 residents), the overall death rate over the course of the pandemic is slightly higher in the least populated parts of the country (an average monthly 15 deaths per 100,000 among the 10% living in the least densely populated counties vs. 13 per 100,000 among the 10% in the most densely populated counties).

As the relationship between population density and coronavirus death rates has changed over the course of the pandemic, so too has the relationship between counties’ voting patterns and their death rates from COVID-19.

In the spring of 2020, the areas recording the greatest numbers of deaths were much more likely to vote Democratic than Republican. But by the third wave of the pandemic, which began in fall 2020, the pattern had reversed: Counties that voted for Donald Trump over Joe Biden were suffering substantially more deaths from the coronavirus pandemic than those that voted for Biden over Trump. This reversal is likely a result of several factors including differences in mitigation efforts and vaccine uptake, demographic differences, and other differences that are correlated with partisanship at the county level.

During this third wave – which continued into early 2021 – the coronavirus death rate among the 20% of Americans living in counties that supported Trump by the highest margins in 2020 was about 170% of the death rate among the one-in-five Americans living in counties that supported Biden by the largest margins.

As vaccines became more widely available, this discrepancy between “blue” and “red” counties became even larger as the virulent delta strain of the pandemic spread across the country during the summer and fall of 2021, even as the total number of deaths fell somewhat from its third wave peak.

During the fourth wave of the pandemic, death rates in the most pro-Trump counties were about four times what they were in the most pro-Biden counties. When the highly transmissible omicron variant began to spread in the U.S. in late 2021, these differences narrowed substantially. However, death rates in the most pro-Trump counties were still about 180% of what they were in the most pro-Biden counties throughout late 2021 and early 2022.

The cumulative impact of these divergent death rates is a wide difference in total deaths from COVID-19 between the most pro-Trump and most pro-Biden parts of the country. Since the pandemic began, counties representing the 20% of the population where Trump ran up his highest margins in 2020 have experienced nearly 70,000 more deaths from COVID-19 than have the counties representing the 20% of population where Biden performed best. Overall, the COVID-19 death rate in all c ounties Trump won in 2020 is substantially higher than it is in counties Biden won (as of the end of February 2022, 326 per 100,000 in Trump counties and 258 per 100,000 in Biden counties).

Partisan divide in COVID-19 deaths widened as more vaccines became available

Partisan differences in COVID-19 death rates expanded dramatically after the availability of vaccines increased. Unvaccinated people are at far higher risk of death and hospitalization from COVID-19, according to the Centers for Disease Control and Prevention, and vaccination decisions are strongly associated with partisanship . Among the large majority of counties for which reliable vaccination data exists, counties that supported Trump at higher margins have substantially lower vaccination rates than those that supported Biden at higher margins.

Counties with lower rates of vaccination registered substantially greater death rates during each wave in which vaccines were widely available.

During the fall of 2021 (roughly corresponding to the delta wave), about 10% of Americans lived in counties with adult vaccination rates lower than 40% as of July 2021. Death rates in these low-vaccination counties were about six times as high as death rates in counties where 70% or more of the adult population was vaccinated.

More Americans were vaccinated heading into the winter of 2021 and 2022 (roughly corresponding to the omicron wave), but nearly 10% of the country lived in areas where less than half of the adult population was vaccinated as of November 2021. Death rates in these low-vaccination counties were roughly twice what they were in counties that had 80% or more of their population vaccinated. ( Note: The statistics here reflect the death rates in the county as a whole, not rates for vaccinated and unvaccinated individuals, though individual-level data finds that death rates among unvaccinated people are far higher than among vaccinated people.)

This analysis relies on official reports of deaths attributed to COVID-19 in the United States collected and reported by The New York Times .

COVID-19 deaths in Puerto Rico and other U.S. territories are not included in this analysis. Additionally, deaths without a specific geographic location have been excluded.

Data was pulled from the GitHub repository maintained by The New York Times on March 1, 2022, and reflects reported coronavirus deaths through Feb. 28.

There are several anomalies in the deaths data. Many locales drop off their reporting on the weekends and holidays. In addition to the rhythm of the reporting cycle, there are many instances where a locality will revise the count of its deaths downward (usually only by a small amount) or release a large batch of previously unreported deaths on a single day. The downward revisions were identified and retroactively applied to earlier days.

Large batches of cases were identified by finding days that increased by more than 10 deaths and were 10 standard deviations above the norm for a county within a 30-day window. Deaths reported in these anomalous batches were then evenly distributed across the days leading up to when they were released.

Population data for U.S. counties comes from the 2015-2019 American Community Survey estimates published by the Census Bureau (accessed through the tidycensus package in R on Feb. 21). The 2020 vote share for each county was purchased from Dave Leip’s Election Atlas (downloaded on Nov. 21, 2021).

The analysis looks at deaths among counties based on their 2020 vote. Counties were grouped into five groups with approximately equal population. For analyses that include 2020 vote, Alaskan counties are excluded because Alaska does not report its election results at the county level. The table below provides more details.

This essay benefited greatly from thoughtful comments and consultation with many individuals around Pew Research Center. Jocelyn Kiley, Carroll Doherty and Jeb Bell provided invaluable editorial guidance. Peter Bell and Alissa Scheller contributed their expertise in visualization, Ben Wormald built the map animation, and Reem Nadeem did the digital production. Andrew Daniller provided careful attention to the quality check process, and David Kent’s watchful copy editing eye brought clarity to some difficult concepts.

Lead photo: Kent Nishimura/Los Angeles Times via Getty Images

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A new, nano-scale look at how the SARS-CoV-2 virus replicates in cells may offer greater precision in drug development, a Stanford University team reports in Nature Communications . Using advanced microscopy techniques, the researchers produced what might be some of the most crisp images available of the virus’s RNA and replication structures, which they witnessed form spherical shapes around the nucleus of the infected cell.

“We have not seen COVID infecting cells at this high resolution and known what we are looking at before,” said Stanley Qi , Stanford associate professor of bioengineering in the Schools of Engineering and of Medicine and co-senior author of the paper. “Being able to know what you are looking at with this high resolution over time is fundamentally helpful to virology and future virus research, including antiviral drug development.”

Blinking RNA 

The work illuminates molecular-scale details of the virus’ activity inside host cells. In order to spread, viruses essentially take over cells and transform them into virus-producing factories, complete with special replication organelles. Within this factory, the viral RNA needs to duplicate itself over and over until enough genetic material is gathered up to move out and infect new cells and start the process over again.

The Stanford scientists sought to reveal this replication step in the sharpest detail to date. To do so, they first labeled the viral RNA and replication-associated proteins with fluorescent molecules of different colors. But imaging glowing RNA alone would result in fuzzy blobs in a conventional microscope. So they added a chemical that temporarily suppresses the fluorescence. The molecules would then blink back on at random times, and only a few lit up at a time. That made it easier to pinpoint the flashes, revealing the locations of the individual molecules.

Using a setup that included lasers, powerful microscopes, and a camera snapping photos every 10 milliseconds, the researchers gathered snapshots of the blinking molecules. When they combined sets of these images, they were able to create finely detailed photos showing the viral RNA and replication structures in the cells. “We have highly sensitive and specific methods and also high resolution,” said Leonid Andronov, co-lead author and Stanford chemistry postdoctoral scholar. “You can see one viral molecule inside the cell.”

The resulting images, with a resolution of 10 nanometers, reveal what might be the most detailed view yet of how the virus replicates itself inside of a cell. The images show magenta RNA forming clumps around the nucleus of the cell, which accumulate into a large repeating pattern. “We are the first to find that viral genomic RNA forms distinct globular structures at high resolution,” said Mengting Han, co-lead author and Stanford bioengineering postdoctoral scholar.

Video showing the different colored fluorescent labels blinking on and off, revealing more precise locations for individual molecules. | Leonid Andronov, Moerner Laboratory

The clusters help show how the virus evades the cell’s defenses, said W. E. Moerner , the paper’s co-senior author and Harry S. Mosher Professor of Chemistry in the School of Humanities and Sciences. “They’re collected together inside a membrane that sequesters them from the rest of the cell, so that they’re not attacked by the rest of the cell.”

Nanoscale drug testing 

Compared to using an electron microscope, the new imaging technique can allow researchers to know with greater certainty where virus components are in a cell thanks to the blinking fluorescent labels. It also can provide nanoscale details of cell processes that are invisible in medical research conducted through biochemical assays. The conventional techniques “are completely different from these spatial recordings of where the objects actually are in the cell, down to this much higher resolution,” said Moerner. “We have an advantage based on the fluorescent labeling because we know where our light is coming from.” 

Seeing exactly how the virus stages its infection holds promise for medicine. Observing how different viruses take over cells may help answer questions such as why some pathogens produce mild symptoms while others are life-threatening. The super-resolution microscopy can also benefit drug development. “This nanoscale structure of the replication organelles can provide some new therapeutic targets for us,” said Han. “We can use this method to screen different drugs and see its influence on the nanoscale structure.”

Indeed, that’s what the team plans to do. They will repeat the experiment and see how the viral structures shift in the presence of drugs like Paxlovid or remdesivir. If a candidate drug can suppress the viral replication step, that suggests the drug is effective at inhibiting the pathogen and making it easier for the host to fight the infection. 

The researchers also plan to map all 29 proteins that make up SARS-CoV-2 and see what those proteins do across the span of an infection. “We hope that we will be prepared to really use these methods for the next challenge to quickly see what’s going on inside and better understand it,” said Qi.

For more information

Acknowledgements: Additional Stanford co-authors include postdoctoral scholar Yanyu Zhu, PhD student Ashwin Balaji, former PhD student Anish Roy, postdoctoral scholar Andrew Barentine, research specialist Puja Patel, and Jaishree Garhyan, director of the In Vitro Biosafety Level-3 Service Center . Moerner is also a member of Stanford Bio-X and the Wu Tsai Neurosciences Institute, and a faculty fellow of Sarafan ChEM-H . Qi is also a member of Bio-X, the Cardiovascular Institute , the Maternal & Child Health Research Institute (MCHRI), the Stanford Cancer Institute, and the Wu Tsai Neurosciences Institute, an institute scholar at Sarafan ChEM-H , and a Chan Zuckerberg Biohub – San Francisco Investigator.

This research was funded by the National Institute of General Medical Sciences of the National Institutes of Health. We also acknowledge use of the Stanford University Cell Sciences Imaging Core Facility.

Taylor Kubota, Stanford University: [email protected]

MINI REVIEW article

Covid-19: emergence, spread, possible treatments, and global burden.

• 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
• 2 Department of Health Sciences, School of Education and Health, Cape Breton University, Sydney, NS, Canada

The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death. Until the outbreak of SARS, this group of viruses was greatly overlooked. However, since the SARS and MERS outbreaks, these viruses have been studied in greater detail, propelling the vaccine research. On December 31, 2019, mysterious cases of pneumonia were detected in the city of Wuhan in China's Hubei Province. On January 7, 2020, the causative agent was identified as a new coronavirus (2019-nCoV), and the disease was later named as COVID-19 by the WHO. The virus spread extensively in the Wuhan region of China and has gained entry to over 210 countries and territories. Though experts suspected that the virus is transmitted from animals to humans, there are mixed reports on the origin of the virus. There are no treatment options available for the virus as such, limited to the use of anti-HIV drugs and/or other antivirals such as Remdesivir and Galidesivir. For the containment of the virus, it is recommended to quarantine the infected and to follow good hygiene practices. The virus has had a significant socio-economic impact globally. Economically, China is likely to experience a greater setback than other countries from the pandemic due to added trade war pressure, which have been discussed in this paper.

Introduction

Coronaviridae is a family of viruses with a positive-sense RNA that possess an outer viral coat. When looked at with the help of an electron microscope, there appears to be a unique corona around it. This family of viruses mainly cause respiratory diseases in humans, in the forms of common cold or pneumonia as well as respiratory infections. These viruses can infect animals as well ( 1 , 2 ). Up until the year 2003, coronavirus (CoV) had attracted limited interest from researchers. However, after the SARS (severe acute respiratory syndrome) outbreak caused by the SARS-CoV, the coronavirus was looked at with renewed interest ( 3 , 4 ). This also happened to be the first epidemic of the 21st century originating in the Guangdong province of China. Almost 10 years later, there was a MERS (Middle East respiratory syndrome) outbreak in 2012, which was caused by the MERS-CoV ( 5 , 6 ). Both SARS and MERS have a zoonotic origin and originated from bats. A unique feature of these viruses is the ability to mutate rapidly and adapt to a new host. The zoonotic origin of these viruses allows them to jump from host to host. Coronaviruses are known to use the angiotensin-converting enzyme-2 (ACE-2) receptor or the dipeptidyl peptidase IV (DPP-4) protein to gain entry into cells for replication ( 7 – 10 ).

In December 2019, almost seven years after the MERS 2012 outbreak, a novel Coronavirus (2019-nCoV) surfaced in Wuhan in the Hubei region of China. The outbreak rapidly grew and spread to neighboring countries. However, rapid communication of information and the increasing scale of events led to quick quarantine and screening of travelers, thus containing the spread of the infection. The major part of the infection was restricted to China, and a second cluster was found on a cruise ship called the Diamond Princess docked in Japan ( 11 , 12 ).

The new virus was identified to be a novel Coronavirus and was thus initially named 2019-nCoV; later, it was renamed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ( 13 ), and the disease it causes is now referred to as Coronavirus Disease-2019 (COVID-19) by the WHO. The virus was suspected to have begun its spread in the Huanan seafood wholesale market in the Wuhan region. It is possible that an animal that was carrying the virus was brought into or sold in the market, causing the spread of the virus in the crowded marketplace. One of the first claims made was in an article published in the Journal of Medical Virology ( 14 ), which identified snakes as the possible host. A second possibility was that pangolins could be the wild host of SARS-CoV-2 ( 15 ), though the most likely possibility is that the virus originated from bats ( 13 , 16 – 19 ). Increasing evidence and experts are now collectively concluding the virus had a natural origin in bats, as with previous such respiratory viruses ( 2 , 20 – 24 ).

Similarly, SARS and MERS were also suspected to originate from bats. In the case of MERS, the dromedary camel is an intermediate host ( 5 , 10 ). Bats have been known to harbor coronaviruses for quite some time now. Just as in the case of avian flu, SARS, MERS, and possibly even HIV, with increasing selection and ecological pressure due to human activities, the virus made the jump from animal to man. Humans have been encroaching increasingly into forests, and this is true over much of China, as in Africa. Combined with additional ecological pressure due to climate change, such zoonotic spillovers are now more common than ever. It is likely that the next disease X will also have such an origin ( 25 ). We have learned the importance of identification of the source organism due to the Ebola virus pandemic. Viruses are unstable organisms genetically, constantly mutating by genetic shift or drift. It is not possible to predict when a cross-species jump may occur and when a seemingly harmless variant form of the virus may turn into a deadly strain. Such an incident occurred in Reston, USA, with the Reston virus ( 26 ), an alarming reminder of this possibility. The identification of the original host helps us to contain future spreads as well as to learn about the mechanism of transmission of viruses. Until the virus is isolated from a wild animal host, in this case, mostly bats, the zoonotic origin will remain hypothetical, though likely. It should further be noted that the virus has acquired several mutations, as noted by a group in China, indicating that there are more than two strains of the virus, which may have had an impact on its pathogenicity. However, this claim remains unproven, and many experts have argued otherwise; data proving this are not yet available ( 27 ). A similar finding was reported from Italy and India independently, where they found two strains ( 28 , 29 ). These findings need to be further cross-verified by similar analyses globally. If true, this finding could effectively explain why some nations are more affected than others.

Transmission

When the spread of COVID-19 began ( Figure 1 ), the virus appeared to be contained within China and the cruise ship “Diamond Princess,” which formed the major clusters of the virus. However, as of April 2020, over 210 countries and territories are affected by the virus, with Europe, the USA, and Iran forming the new cluster of the virus. The USA ( Figure 2 ) has the highest number of confirmed COVID-19 cases, whereas India and China, despite being among the most population-dense countries in the world, have managed to constrain the infection rate by the implementation of a complete lockdown with arrangements in place to manage the confirmed cases. Similarly, the UK has also managed to maintain a low curve of the graph by implementing similar measures, though it was not strictly enforced. Reports have indicated that the presence of different strains or strands of the virus may have had an effect on the management of the infection rate of the virus ( 27 – 29 ). The disease is spread by droplet transmission. As of April 2020, the total number of infected individuals stands at around 3 million, with ~200,000 deaths and more than 1 million recoveries globally ( 30 , 34 ). The virus thus has a fatality rate of around 2% and an R 0 of 3 based on current data. However, a more recent report from the CDC, Atlanta, USA, claims that the R 0 could be as high as 5.7 ( 35 ). It has also been observed from data available from China and India that individuals likely to be infected by the virus from both these countries belong to the age groups of 20–50 years ( 36 , 37 ). In both of these countries, the working class mostly belongs to this age group, making exposure more likely. Germany and Singapore are great examples of countries with a high number of cases but low fatalities as compared to their immediate neighbors. Singapore is one of the few countries that had developed a detailed plan of action after the previous SARS outbreak to deal with a similar situation in the future, and this worked in their favor during this outbreak. Both countries took swift action after the outbreak began, with Singapore banning Chinese travelers and implementing screening and quarantine measures at a time when the WHO recommended none. They ordered the elderly and the vulnerable to strictly stay at home, and they ensured that lifesaving equipment and large-scale testing facilities were available immediately ( 38 , 39 ). Germany took similar measures by ramping up testing capacity quite early and by ensuring that all individuals had equal opportunity to get tested. This meant that young, old, and at-risk people all got tested, thus ensuring positive results early during disease progression and that most cases were mild like in Singapore, thus maintaining a lower death percentage ( 40 ). It allowed infected individuals to be identified and quarantined before they even had symptoms. Testing was carried out at multiple labs, reducing the load and providing massive scale, something which countries such as the USA did quite late and India restricted to select government and private labs. The German government also banned large gatherings and advocated social distancing to further reduce the spread, though unlike India and the USA, this was done quite late. South Korea is another example of how a nation has managed to contain the spread and transmission of the infection. South Korea and the USA both reported their first COVID-19 cases on the same day; however, the US administration downplayed the risks of the disease, unlike South Korean officials, who constantly informed their citizens about the developments of the disease using the media and a centralized messaging system. They also employed the Trace, Test, and Treat protocol to identify and isolate patients fast, whereas the USA restricted this to patients with severe infection and only later broadened this criterion, like many European countries as well as India. Unlike the USA, South Korea also has universal healthcare, ensuring free diagnostic testing.

Figure 1 . Timeline of COVID-19 progression ( 30 – 32 ).

Figure 2 . Total confirmed COVID 19 cases as of May 2020 ( 33 ).

The main mode of transmission of 2019-nCoV is human to human. As of now, animal-to-human transfer has not yet been confirmed. Asymptomatic carriers of the virus are at major risk of being superinfectors with this disease, as all those infected may not develop the disease ( 41 ). This is a concern that has been raised by nations globally, with the Indian government raising concerns on how to identify and contain asymptomatic carriers, who could account for 80% of those infected ( 42 ). Since current resources are directed towards understanding the hospitalized individuals showing symptoms, there is still a vast amount of information about asymptomatic individuals that has yet to be studied. For example, some questions that need to be answered include: Do asymptomatic individuals develop the disease at any point in time at all? Do they eventually develop antibodies? How long do they shed the virus for? Can any tissue of these individuals store the virus in a dormant state? Asymptomatic transmission is a gray area that encompasses major unknowns in COVID-19.

The main route of human-to-human transmission is by droplets, which are generated during coughing, talking, or sneezing and are then inhaled by a healthy individual. They can also be indirectly transmitted to a person when they land on surfaces that are touched by a healthy individual who may then touch their nose, mouth, or eyes, allowing the virus entry into the body. Fomites are also a common issue in such diseases ( 43 ).

Aerosol-based transmission of the virus has not yet been confirmed ( 43 ). Stool-based transmission via the fecal-oral route may also be possible since the SARS-CoV-2 has been found in patient feces ( 44 , 45 ). Some patients with COVID-19 tend to develop diarrhea, which can become a major route of transmission if proper sanitation and personal hygiene needs are not met. There is no evidence currently available to suggest intrauterine vertical transmission of the disease in pregnant women ( 46 ).

More investigation is necessary of whether climate has played any role in the containment of the infection in countries such as India, Singapore, China, and Israel, as these are significantly warmer countries as compared with the UK, the USA, and Canada ( Figure 2 ). Ideally, a warm climate should prevent the virus from surviving for longer periods of time on surfaces, reducing transmissibility.

Pathophysiology

On gaining entry via any of the mucus membranes, the single-stranded RNA-based virus enters the host cell using type 2 transmembrane serine protease (TMPRSS2) and ACE2 receptor protein, leading to fusion and endocytosis with the host cell ( 47 – 49 ). The uncoated RNA is then translated, and viral proteins are synthesized. With the help of RNA-dependant RNA polymerase, new RNA is produced for the new virions. The cell then undergoes lysis, releasing a load of new virions into the patients' body. The resultant infection causes a massive release of pro-inflammatory cytokines that causes a cytokine storm.

Clinical Presentation

The clinical presentation of the disease resembles beta coronavirus infections. The virus has an incubation time of 2–14 days, which is the reason why most patients suspected to have the illness or contact with an individual having the illness remain in quarantine for the said amount of time. Infection with SARS-CoV-2 causes severe pneumonia, intermittent fever, and cough ( 50 , 51 ). Symptoms of rhinorrhoea, pharyngitis, and sneezing have been less commonly seen. Patients often develop acute respiratory distress syndrome within 2 days of hospital admission, requiring ventilatory support. It has been observed that during this phase, the mortality tends to be high. Chest CT will show indicators of pneumonia and ground-glass opacity, a feature that has helped to improve the preliminary diagnosis ( 51 ). The primary method of diagnosis for SARS-CoV-2 is with the help of PCR. For the PCR testing, the US CDC recommends testing for the N gene, whereas the Chinese CDC recommends the use of ORF lab and N gene of the viral genome for testing. Some also rely on the radiological findings for preliminary screening ( 52 ). Additionally, immunodiagnostic tests based on the presence of antibodies can also play a role in testing. While the WHO recommends the use of these tests for research use, many countries have pre-emptively deployed the use of these tests in the hope of ramping up the rate and speed of testing ( 52 – 54 ). Later, they noticed variations among the results, causing them to stop the use of such kits; there was also debate among the experts about the sensitivity and specificity of the tests. For immunological tests, it is beneficial to test for antibodies against the virus produced by the body rather than to test for the presence of the viral proteins, since the antibodies can be present in larger titers for a longer span of time. However, the cross-reactivity of these tests with other coronavirus antibodies is something that needs verification. Biochemical parameters such as D-dimer, C-reactive protein, and variations in neutrophil and lymphocyte counts are some other parameters that can be used to make a preliminary diagnosis; however, these parameters vary in a number of diseases and thus cannot be relied upon conclusively ( 51 ). Patients with pre-existing diseases such as asthma or similar lung disorder are at higher risk, requiring life support, as are those with other diseases such as diabetes, hypertension, or obesity. Those above the age of 60 have displayed the highest mortality rate in China, a finding that is mirrored in other nations as well ( Figure 3 ) ( 55 ). If we cross-verify these findings with the population share that is above the age of 70, we find that Italy, the United Kingdom, Canada, and the USA have one of the highest elderly populations as compared to countries such as India and China ( Figure 4 ), and this also reflects the case fatality rates accordingly ( Figure 5 ) ( 33 ). This is a clear indicator that aside from comorbidities, age is also an independent risk factor for death in those infected by COVID-19. Also, in the US, it was seen that the rates of African American deaths were higher. This is probably due to the fact that the prevalence of hypertension and obesity in this community is higher than in Caucasians ( 56 , 57 ). In late April 2020, there are also claims in the US media that young patients in the US with COVID-19 may be at increased risk of stroke; however, this is yet to be proven. We know that coagulopathy is a feature of COVID-19, and thus stroke is likely in this condition ( 58 , 59 ). The main cause of death in COVID-19 patients was acute respiratory distress due to the inflammation in the linings of the lungs caused by the cytokine storm, which is seen in all non-survival cases and in respiratory failure. The resultant inflammation in the lungs, served as an entry point of further infection, associated with coagulopathy end-organ failure, septic shock, and secondary infections leading to death ( 60 – 63 ).

Figure 3 . Case fatality rate by age in selected countries as of April 2020 ( 33 ).

Figure 4 . Case fatality rate in selected countries ( 33 ).

Figure 5 . Population share above 70 years of age ( 33 ).

For COVID-19, there is no specific treatment available. The WHO announced the organization of a trial dubbed the “Solidarity” clinical trial for COVID-19 treatments ( 64 ). This is an international collaborative study that investigates the use of a few prime candidate drugs for use against COVID-19, which are discussed below. The study is designed to reduce the time taken for an RCT by over 80%. There are over 1087 studies ( Supplementary Data 1 ) for COVID-19 registered at clinicaltrials.gov , of which 657 are interventional studies ( Supplementary Data 2 ) ( 65 ). The primary focus of the interventional studies for COVID-19 has been on antimalarial drugs and antiviral agents ( Table 1 ), while over 200 studies deal with the use of different forms of oxygen therapy. Most trials focus on improvement of clinical status, reduction of viral load, time to improvement, and reduction of mortality rates. These studies cover both severe and mild cases.

Table 1 . List of therapeutic drugs under study for COVID-19 as per clinical trials registered under clinicaltrials.gov .

Use of Antimalarial Drugs Against SARS-CoV-2

The use of chloroquine for the treatment of corona virus-based infection has shown some benefit in the prevention of viral replication in the cases of SARS and MERS. However, it was not validated on a large scale in the form of a randomized control trial ( 50 , 66 – 68 ). The drugs of choice among antimalarials are Chloroquine (CQ) and Hydroxychloroquine (HCQ). The use of CQ for COVID-19 was brought to light by the Chinese, especially by the publication of a letter to the editor of Bioscience Trends by Gao et al. ( 69 ). The letter claimed that several studies found CQ to be effective against COVID-19; however, the letter did not provide many details. Immediately, over a short span of time, interest in these two agents grew globally. Early in vitro data have revealed that chloroquine can inhibit the viral replication ( 70 , 71 ).

HCQ and CQ work by raising the pH of the lysosome, the cellular organelle that is responsible for phagocytic degradation. Its function is to combine with cell contents that have been phagocytosed and break them down eventually, in some immune cells, as a downstream process to display some of the broken proteins as antigens, thus further enhancing the immune recruitment against an antigen/pathogen. The drug was to be administered alone or with azithromycin. The use of azithromycin may be advocated by the fact that it has been seen previously to have some immunomodulatory role in airway-related disease. It appears to reduce the release of pro-inflammatory cytokines in respiratory illnesses ( 72 ). However, HCQ and azithromycin are known to have a major drug interaction when co-administered, which increases the risk of QT interval prolongation ( 73 ). Quinine-based drugs are known to have adverse effects such as QT prolongation, retinal damage, hypoglycemia, and hemolysis of blood in patients with G-6-PD deficiency ( 66 ). Several preprints, including, a metanalysis now indicate that HCQ may have no benefit for severe or critically ill patients who have COVID-19 where the outcome is need for ventilation or death ( 74 , 75 ). As of April 21, 2020, after having pre-emptively recommended their use for SARS-CoV-2 infection, the US now advocates against the use of these two drugs based on the new data that has become available.

Use of Antiviral Drugs Against SARS-CoV-2

The antiviral agents are mainly those used in the case of HIV/AIDS, these being Lopinavir and Ritonavir. Other agents such as nucleoside analogs like Favipiravir, Ribavirin, Remdesivir, and Galidesivir have been tested for possible activity in the prevention of viral RNA synthesis ( 76 ). Among these drugs, Lopinavir, Ritonavir, and Remdesivir are listed in the Solidarity trial by the WHO.

Remdesivir is a nucleotide analog for adenosine that gets incorporated into the viral RNA, hindering its replication and causing chain termination. This agent was originally developed for Ebola Virus Disease ( 77 ). A study was conducted with rhesus macaques infected with SARS-CoV-2 ( 78 ). In that study, after 12 h of infection, the monkeys were treated with either Remdesivir or vehicle. The drug showed good distribution in the lungs, and the animals treated with the drug showed a better clinical score than the vehicle group. The radiological findings of the study also indicated that the animals treated with Remdesivir have less lung damage. There was a reduction in viral replication but not in virus shedding. Furthermore, there were no mutations found in the RNA polymerase sequences. A randomized clinical control study that became available in late April 2020 ( 79 ), having 158 on the Remdesivir arm and 79 on the placebo arm, found that Remdesivir reduced the time to recovery in the Remdesivir-treated arm to 11 days, while the placebo-arm recovery time was 15 days. Though this was not found to be statistically significant, the agent provided a basis for further studies. The 28-days mortality was found to be similar for both groups. This has now provided us with a basis on which to develop future molecules. The study has been supported by the National Institute of Health, USA. The authors of the study advocated for more clinical trials with Remdesivir with a larger population. Such larger studies are already in progress, and their results are awaited. Remdesivir is currently one of the drugs that hold most promise against COVID-19.

An early trial in China with Lopinavir and Ritonavir showed no benefit compared with standard clinical care ( 80 ). More studies with this drug are currently underway, including one in India ( 81 , 82 ).

Use of Convalescent Patient Plasma

Another possible option would be the use of serum from convalescent individuals, as this is known to contain antibodies that can neutralize the virus and aid in its elimination. This has been tried previously for other coronavirus infections ( 83 ). Early emerging case reports in this aspect look promising compared to other therapies that have been tried ( 84 – 87 ). A report from China indicates that five patients treated with plasma recovered and were eventually weaned off ventilators ( 84 ). They exhibited reductions in fever and viral load and improved oxygenation. The virus was not detected in the patients after 12 days of plasma transfusion. The US FDA has provided detailed recommendations for investigational COVID-19 Convalescent Plasma use ( 88 ). One of the benefits of this approach is that it can also be used for post-exposure prophylaxis. This approach is now beginning to be increasingly adopted in other countries, with over 95 trials registered on clinicaltrials.gov alone, of which at least 75 are interventional ( 89 ). The use of convalescent patient plasma, though mostly for research purposes, appears to be the best and, so far, the only successful option for treatment available.

From a future perspective, the use of monoclonal antibodies for the inhibition of the attachment of the virus to the ACE-2 receptor may be the best bet. Aside from this, ACE-2-like molecules could also be utilized to attach and inactivate the viral proteins, since inhibition of the ACE-2 receptor would not be advisable due to its negative repercussions physiologically. In the absence of drug regimens and a vaccine, the treatment is symptomatic and involves the use of non-invasive ventilation or intubation where necessary for respiratory failure patients. Patients that may go into septic shock should be managed as per existing guidelines with hemodynamic support as well as antibiotics where necessary.

The WHO has recommended that simple personal hygiene practices can be sufficient for the prevention of spread and containment of the disease ( 90 ). Practices such as frequent washing of soiled hands or the use of sanitizer for unsoiled hands help reduce transmission. Covering of mouth while sneezing and coughing, and disinfection of surfaces that are frequently touched, such as tabletops, doorknobs, and switches with 70% isopropyl alcohol or other disinfectants are broadly recommended. It is recommended that all individuals afflicted by the disease, as well as those caring for the infected, wear a mask to avoid transmission. Healthcare works are advised to wear a complete set of personal protective equipment as per WHO-provided guidelines. Fumigation of dormitories, quarantine rooms, and washing of clothes and other fomites with detergent and warm water can help get rid of the virus. Parcels and goods are not known to transmit the virus, as per information provided by the WHO, since the virus is not able to survive sufficiently in an open, exposed environment. Quarantine of infected individuals and those who have come into contact with an infected individual is necessary to further prevent transmission of the virus ( 91 ). Quarantine is an age-old archaic practice that continues to hold relevance even today for disease containment. With the quarantine being implemented on such a large scale in some countries, taking the form of a national lockdown, the question arises of its impact on the mental health of all individuals. This topic needs to be addressed, especially in countries such as India and China, where it is still a matter of partial taboo to talk about it openly within the society.

In India, the Ministry of Ayurveda, Yoga, and Naturopathy, Unani, Siddha and Homeopathy (AYUSH), which deals with the alternative forms of medicine, issued a press release that the homeopathic, drug Arsenicum album 30, can be taken on an empty stomach for 3 days to provide protection against the infection ( 92 ). It also provided a list of herbal drugs in the same press release as per Ayurvedic and Unani systems of medicine that can boost the immune system to deal with the virus. However, there is currently no evidence to support the use of these systems of medicine against COVID-19, and they need to be tested.

The prevention of the disease with the use of a vaccine would provide a more viable solution. There are no vaccines available for any of the coronaviruses, which includes SARS and MERS. The development of a vaccine, however, is in progress at a rapid pace, though it could take about a year or two. As of April 2020, no vaccine has completed the development and testing process. A popular approach has been with the use of mRNA-based vaccine ( 93 – 96 ). mRNA vaccines have the advantage over conventional vaccines in terms of production, since they can be manufactured easily and do not have to be cultured, as a virus would need to be. Alternative conventional approaches to making a vaccine against SARS-CoV-2 would include the use of live attenuated virus as well as using the isolated spike proteins of the virus. Both of these approaches are in progress for vaccine development ( 97 ). Governments across the world have poured in resources and made changes in their legislation to ensure rapid development, testing, and deployment of a vaccine.

Barriers to Treatment

Lack of transparency and poor media relations.

The lack of government transparency and poor reporting by the media have hampered the measures that could have been taken by healthcare systems globally to deal with the COVID-19 threat. The CDC, as well as the US administration, downplayed the threat and thus failed to stock up on essential supplies, ventilators, and test kits. An early warning system, if implemented, would have caused borders to be shut and early lockdowns. The WHO also delayed its response in sounding the alarm regarding the severity of the outbreak to allow nations globally to prepare for a pandemic. Singapore is a prime example where, despite the WHO not raising concerns and banning travel to and from China, a country banned travelers and took early measures, thus managing the outbreak quite well. South Korea is another example of how things may have played out had those measures by agencies been taken with transparency. Increased transparency would have allowed the healthcare sector to better prepare and reduced the load of patients they had to deal with, helping flatten the curve. The increased patient load and confusion among citizens arising from not following these practices has proved to be a barrier to providing effective treatments to patients with the disease elsewhere in the world.

Lack of Preparedness and Protocols

Despite the previous SARS outbreak teaching us important lessons and providing us with data on a potential outbreak, many nations did not take the important measures needed for a future outbreak. There was no allocation of sufficient funds for such an event. Many countries experienced severe lack of PPE, and the lockdown precautions hampered the logistics of supply and manufacturing of such essential equipment. Singapore and South Korea had protocols in place and were able to implement them at a moment's notice. The spurt of cases that Korea experienced was managed well, providing evidence to this effect. The lack of preparedness and lack of protocol in other nations has resulted in confusion as to how the treatment may be administered safely to the large volume of patients while dealing with diagnostics. Both of these factors have limited the accessibility to healthcare services due to sheer volume.

Socio-Economic Impact

During the SARS epidemic, China faced an economic setback, and experts were unsure if any recovery would be made. However, the global and domestic situation was then in China's favor, as it had a lower debt, allowing it to make a speedy recovery. This is not the case now. Global experts have a pessimistic outlook on the outcome of this outbreak ( 98 ). The fear of COVID-19 disease, lack of proper understanding of the dangers of the virus, and the misinformation spread on the social media ( 99 ) have caused a breakdown of the economic flow globally ( 100 ). An example of this is Indonesia, where a great amount of fear was expressed in responses to a survey when the nation was still free of COVID-19 ( 101 ). The pandemic has resulted in over 2.6 billion people being put under lockdown. This lockdown and the cancellation of the lunar year celebration has affected business at the local level. Hundreds of flights have been canceled, and tourism globally has been affected. Japan and Indonesia are estimated to lose over 2.44 billion dollars due to this ( 102 , 103 ). Workers are not able to work in factories, transportation in all forms is restricted, and goods are not produced or moved. The transport of finished products and raw materials out of China is low. The Economist has published US stock market details indicating that companies in the US that have Chinese roots fell, on average, 5 points on the stock market as compared to the S&P 500 index ( 104 ). Companies such as Starbucks have had to close over 4,000 outlets due to the outbreak as a precaution. Tech and pharma companies are at higher risk since they rely on China for the supply of raw materials and active pharmaceutical ingredients. Paracetamol, for one, has reported a price increase of over 40% in India ( 104 – 106 ). Mass hysteria in the market has caused selling of shares of these companies, causing a tumble in the Indian stock market. Though long-term investors will not be significantly affected, short-term traders will find themselves in soup. Politically, however, this has further bolstered support for world leaders in countries such as India, Germany, and the UK, who are achieving good approval ratings, with citizens being satisfied with the government's approach. In contrast, the ratings of US President Donald Trump have dropped due to the manner in which the COVID-19 pandemic was handled. These minor impacts may be of temporary significance, and the worst and direct impact will be on China itself ( 107 – 109 ), as the looming trade war with the USA had a negative impact on the Chinese and Asian markets. The longer production of goods continues to remain suspended, the more adversely it will affect the Chinese economy and the global markets dependent on it ( 110 ). If this disease is not contained, more and more lockdowns by multiple nations will severely affect the economy and lead to many social complications.

The appearance of the 2019 Novel Coronavirus has added and will continue to add to our understanding of viruses. The pandemic has once again tested the world's preparedness for dealing with such outbreaks. It has provided an outlook on how a massive-scale biological event can cause a socio-economic disturbance through misinformation and social media. In the coming months and years, we can expect to gain further insights into SARS-CoV-2 and COVID-19.

Author Contributions

KN: conceptualization. RK, AA, JM, and KN: investigation. RK and AA: writing—original draft preparation. KN, PN, and JM: writing—review and editing. KN: supervision.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge the contributions made by Dr. Piya Paul Mudgal, Assistant Professor, Manipal Institute of Virology, Manipal Academy of Higher Education towards inputs provided by her during the drafting of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00216/full#supplementary-material

Supplementary Data 1, 2. List of all studies registered for COVID-19 on clinicaltrials.gov .

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Keywords: 2019-nCoV, COVID-19, SARS-CoV-2, coronavirus, pandemic, SARS

Citation: Keni R, Alexander A, Nayak PG, Mudgal J and Nandakumar K (2020) COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. Front. Public Health 8:216. doi: 10.3389/fpubh.2020.00216

Received: 21 February 2020; Accepted: 11 May 2020; Published: 28 May 2020.

Reviewed by:

Copyright © 2020 Keni, Alexander, Nayak, Mudgal and Nandakumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Krishnadas Nandakumar, mailnandakumar77@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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This tracker provides the cumulative number of confirmed COVID-19 cases and deaths, as well as the rate of daily COVID-19 cases and deaths by country, income, region, and globally. It will be updated weekly, as new data are released. As of March 7, 2023, all data on COVID-19 cases and deaths are drawn from the World Health Organization’s (WHO) Coronavirus (COVID-19) Dashboard . Prior to March 7, 2023, this tracker relied on data provided by the Johns Hopkins University (JHU) Coronavirus Resource Center’s COVID-19 Map, which ended on March 10, 2023. Please see the Methods tab for more detailed information on data sources and notes. To prevent slow load times, the tracker only contains data from the last 200 days. However, the full data set can be downloaded from our GitHub page .

Note: The data in this tool were corrected on March 18, 2024, to clarify that they represent new cases and deaths over a full week rather than the average per day over a seven-day period.

• Coronavirus (COVID-19)
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Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

Due to the downward trend in respiratory viruses in Maryland, masking is no longer required but remains strongly recommended in Johns Hopkins Medicine clinical locations in Maryland. Read more .

• Vaccines  
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What Is Coronavirus?

Coronaviruses are a type of virus. There are many different kinds, and some cause disease. A coronavirus identified in 2019, SARS-CoV-2, has caused a pandemic of respiratory illness, called COVID-19.

What You Need to Know COVID-19

• COVID-19 is the disease caused by SARS-CoV-2, the coronavirus that emerged in December 2019.
• COVID-19 can be severe, and has caused millions of deaths around the world as well as lasting health problems in some who have survived the illness.
• The coronavirus can be spread from person to person. It is diagnosed with a test.
• The best way to protect yourself is to get vaccinated and boosted when you are eligible, follow testing guidelines, wear a mask, wash your hands and practice physical distancing.

How does the coronavirus spread?

As of now, researchers know that the coronavirus is spread through droplets and virus particles released into the air when an infected person breathes, talks, laughs, sings, coughs or sneezes. Larger droplets may fall to the ground in a few seconds, but tiny infectious particles can linger in the air and accumulate in indoor places, especially where many people are gathered and there is poor ventilation. This is why mask-wearing, hand hygiene and physical distancing are essential to preventing COVID-19.

How did the coronavirus start?

The first case of COVID-19 was reported Dec. 1, 2019, and the cause was a then-new coronavirus later named SARS-CoV-2. SARS-CoV-2 may have originated in an animal and changed (mutated) so it could cause illness in humans. In the past, several infectious disease outbreaks have been traced to viruses originating in birds, pigs, bats and other animals that mutated to become dangerous to humans. Research continues, and more study may reveal how and why the coronavirus evolved to cause pandemic disease.

What is the incubation period for COVID-19?

Symptoms show up in people within two to 14 days of exposure to the virus. A person infected with the coronavirus is contagious to others for up to two days before symptoms appear, and they remain contagious to others for 10 to 20 days, depending upon their immune system and the severity of their illness. 

What have you learned about coronavirus in the last six months?

Infectious disease expert Lisa Maragakis explains the advances in COVID-19 treatments and how knowledge of COVID-19 can assist in preventing further spread of the virus.

What are symptoms of coronavirus?

COVID-19 symptoms include:

• Fever or chills
• Shortness of breath or difficulty breathing
• Muscle or body aches
• Sore throat
• New loss of taste or smell
• New fatigue
• Nausea or vomiting
• Congestion or runny nose

Some people infected with the coronavirus have mild COVID-19 illness, and others have no symptoms at all. In some cases, however, COVID-19 can lead to respiratory failure, lasting  lung  and  heart muscle damage ,  nervous system problems ,  kidney failure  or death.

If you have a fever or any of the symptoms listed above, call your doctor or a health care provider and explain your symptoms over the phone before going to the doctor’s office, urgent care facility or emergency room. Here are suggestions  if you feel sick and are concerned you might have COVID-19 .

CALL 911 if you have a medical emergency such as severe shortness of breath or difficulty breathing.

Learn more about COVID-19 symptoms .

How is COVID-19 diagnosed?

COVID-19 is diagnosed through a test. Diagnosis by examination alone is difficult since many COVID-19 signs and symptoms can be caused by other illnesses. Some people with the coronavirus do not have symptoms at all.  Learn more about COVID-19 testing .

How is COVID-19 treated?

Treatment for COVID-19 depends on the severity of the infection. For milder illness, resting at home and taking medicine to reduce fever is often sufficient. More severe cases may require hospitalization, with treatment that might include intravenous medications, supplemental oxygen, assisted ventilation and other supportive measures

How do you protect yourself from this?

There are several COVID-19 vaccines recommended by the CDC . It is also important to receive a booster when you are eligible .

In addition, it helps to keep up with other safety precautions, such as following testing guidelines, wearing a mask, washing your hands and practicing physical distancing.

Does COVID-19 cause death?

Yes, severe COVID-19 can be fatal. For updates of coronavirus infections, deaths and vaccinations worldwide, see the  Coronavirus COVID-19 Global Cases  map developed by the Johns Hopkins Center for Systems Science and Engineering.

Two COVID-19 vaccines – Pfizer and Moderna - have been fully approved by the FDA and recommended by the CDC as highly effective in preventing serious disease, hospitalization and death from COVID-19.

The CDC notes that in most situations the two mRNA vaccines from Pfizer and Moderna are preferred over the Johnson & Johnson vaccine due to a risk of serious adverse events .

It is also important to receive a booster when eligible. You can get any of these three authorized or approved vaccines, but the CDC explains that Pfizer and Moderna are preferred in most situations.

Why is it called coronavirus?

Coronaviruses are named for their appearance: “corona” means “crown.” The virus’s outer layers are covered with spike proteins that surround them like a crown.

Is this coronavirus different from SARS?

SARS  stands for severe acute respiratory syndrome. In 2003, an outbreak of SARS affected people in several countries before ending in 2004. The coronavirus that causes COVID-19 is similar to the one that caused the 2003 SARS outbreak.

Since the 2019 coronavirus is related to the original coronavirus that caused SARS and can also cause severe acute respiratory syndrome, there is “SARS” in its name: SARS-CoV-2. Much is still unknown about these viruses, but SARS-CoV-2 spreads faster and farther than the 2003 SARS-CoV-1 virus. This is likely because of how easily it is transmitted person to person, even from asymptomatic carriers of the virus.

Are there different variants of this coronavirus?

Yes, there are different variants of this coronavirus. Like other viruses, the coronavirus that causes COVID-19 can change (mutate). Mutations may enable the coronavirus to spread faster from person to person as in the case of the delta and omicron variants. More infections can result in more people getting very sick and also create more opportunity for the virus to develop further mutations. Read more about  coronavirus variants .

Coronavirus: What do I do if I Feel Sick?

If you are concerned that you may have COVID-19, follow these steps to help protect your health and the health of others.

About Coronaviruses

• Coronaviruses are common in different animals. Rarely, an animal coronavirus can infect humans.
• There are many different kinds of coronaviruses. Some of them can cause colds or other mild respiratory (nose, throat, lung) illnesses.
• Other coronaviruses can cause serious diseases, including severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS).

Coronavirus (COVID-19)

What you need to know from Johns Hopkins Medicine.

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• Published: 23 February 2023

Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources

• Ginger Tsueng   ORCID: orcid.org/0000-0001-9536-9115 1 ,
• Julia L. Mullen 1 ,
• Manar Alkuzweny   ORCID: orcid.org/0000-0002-6069-5778 2 , 3 ,
• Marco Cano 1 ,
• Benjamin Rush 4 ,
• Emily Haag 1 ,
• Jason Lin 1 ,
• Dylan J. Welzel 1 ,
• Xinghua Zhou   ORCID: orcid.org/0000-0002-9119-3906 1 ,
• Zhongchao Qian   ORCID: orcid.org/0000-0001-8334-9467 1 ,
• Alaa Abdel Latif   ORCID: orcid.org/0000-0002-3713-8420 3 ,
• Emory Hufbauer 3 ,
• Mark Zeller 3 ,
• Kristian G. Andersen 3 , 5 ,
• Chunlei Wu   ORCID: orcid.org/0000-0002-2629-6124 1 , 5 , 6 ,
• Andrew I. Su 1 , 5 , 6 ,
• Karthik Gangavarapu   ORCID: orcid.org/0000-0002-5027-3440 3 , 7 &
• Laura D. Hughes   ORCID: orcid.org/0000-0003-1718-6676 1  

Nature Methods volume  20 ,  pages 536–540 ( 2023 ) Cite this article

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Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.

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COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations

In January 2020, SARS-CoV-2 was identified as the virus responsible for a series of pneumonia cases with unknown origin 1 . As the virus spread globally, the scientific community rapidly released research outputs (such as publications, clinical trials and datasets) and resources (websites, portals and more). The frequently uncoordinated generation and curation of resources exacerbated four challenges in finding and using them: volume, fragmentation, variety and standardization (Supplementary Fig. 1 ). While many specialized websites were developed independently 2 , 3 , 4 , 5 , 6 , 7 , a centralized and standardized repository for finding COVID-19 research has limited researchers’ ability to discover these resources and translate them into insights about the virus.

To address the fragmented research landscape, individual and community efforts created shared Google spreadsheets 8 , 9 , 10 to aid in discoverability, but these efforts were not scalable and often lacked metadata to promote findability (aside from Navarro and Capdarest-Arest 10 ). Several projects attempted to address the volume and fragmentation issues through large-scale aggregation but failed to tackle variety, focusing on a single resource type such as publications 11 , 12 . Even within a particular type of resource, standardization issues abound. Repositories pivoted quickly to curate COVID-19 content from their collections using pre-existing metadata standards but were often not interoperable with other sources. For example, PubMed created LitCovid 13 based on their MEDLINE standards, and the National Clinical Trials Registry cataloged COVID-19 clinical trials using their schema 14 , but the World Health Organization (WHO) International Clinical Trials Registry uses different conventions. Similarly, Zenodo 15 and Figshare 16 do not agree on the marginality, cardinality and property names 17 , 18 , despite compatibility with the standards of https://schema.org .

We address issues in metadata volume, variety, standardization and fragmentation by creating a single searchable index of COVID-19 publications, clinical trials, datasets and more: the outbreak.info Research Library. To address variety and standardization, we developed a harmonized schema based on https://schema.org , a framework standardizing metadata across the internet. Using this schema, we harvested and harmonized metadata from 16 resources (Fig. 1a ). Daily updates ensure that site users have up-to-date information, essential amid a constantly changing research landscape.

a , Distribution of resources by resource type and source. ICTRP, International Clinical Trials Registry Platform; MRC GIDA, MRC Centre for Global Infectious Disease Analysis. b , Searching for ‘Delta variant’ finds heterogeneous resources.

Next, to address volume and fragmentation, we developed a web-based search portal for researchers to browse across the centralized and standardized resources ( https://outbreak.info/resources ) and an API to access and analyze information en masse ( https://api.outbreak.info ). Within the search interface, users can search, filter and view related records and share the associated metadata to easily query across resource repositories and types. For instance, a single query (for example, ‘Delta variant’) to our API can return relevant publications, datasets, clinical trials and more (Fig. 1b ), and the Research Library summarizes the search results in visualizations to promote exploration. For instance, the histogram in Fig. 1b indicates that the number of resources mentioning ‘Delta variant’ began growing in mid 2021 and declined in the summer of 2022, and the donut charts show that LitCovid is the dominant source. To ensure ease of use of our Research Library, we conducted usability studies and iteratively improved our site (Supplementary Fig. 2 ).

To further address fragmentation and maintenance issues, we use modular infrastructure, allowing easy addition of new data sources, including community contributions. Citizen scientists have played an active role in data collection 19 ( https://covidsample.org/ ) and accessibility 12 , 20 throughout the pandemic. Given the highly fragmented, diffuse and frequently changing nature inherent to biomedical research, we built in three mechanisms to expand the Research Library through community participation (Supplementary Fig. 3a ).

First, contributors can submit individual or multiple datasets via an online form that ensures that the curated metadata conform to our schema. Second, leveraging the benefits of human curation, the community-contributed metadata using the form can be exhaustively detailed (Supplementary Fig. 3b ) and can further be augmented through pull requests on GitHub. Lastly, anyone with Python coding skills can submit collections of standardized datasets, publications and other resources to the Outbreak Resources API by contributing a resource parser. Our community-contribution pipeline allows us to integrate the uncoordinated data-curation efforts quickly and flexibly, particularly apparent at the start of the pandemic (Supplementary Fig. 4 ).

To support resource exploration and interpretation, we added properties (value-added metadata) to every class in our schema that would support searching, filtering and browsing (topicCategories, Supplementary Fig. 5a ); linkage and exploration (correction, citedBy, isBasedOn, isRelatedTo; Supplementary Fig. 5b ); and interpretation (qualitative evaluations) of resources. We selected these properties based on pre-existing citizen science- and resource-curation activities, suggesting their value in promoting discoverability. For example, citizen scientists categorized resources in their lists or collections by type (Dataset, ClinicalTrials, etc.) in their outputs 10 or area of research (epidemiological, prevention, etc.) 20 as they found these classifications helpful for searching, filtering and browsing their lists or collections. Given the ability of citizen scientists to perform information extraction 21 and their immense contributions to classification tasks 22 , we incorporated citizen science contributions into the training data for classifying resources into topic categories. Citizen scientists also provided Oxford 2011 Levels of Evidence annotations to improve its interpretability (that is, understanding the credibility or quality of the resource) 20 . To further enable assessment of the quality of a resource, we leveraged Digital Science’s Altmetric ratings 23 .

Finally, we integrated resources with the analyses that we developed to track SARS-CoV-2 variants of concern (VOCs) 24 , sets of mutations within the virus associated with increased transmissibility, virulence and/or immune evasion. Researchers can seamlessly traverse from a specific variant report such as Omicron to resources in the Research Library that help understand its behavior (Supplementary Fig. 5c ), and variant searches are among our most commonly queried terms (Supplementary Table 1a ). Without a centralized search interface with linked records such as outbreak.info, a similar attempt to explore resources would require extensive manual searching from multiple different sites (Supplementary Fig. 6 ), each with their own interfaces and corresponding search capabilities.

To demonstrate the unique features of the outbreak.info Research Library, we explored the dynamics of research into SARS-CoV-2 variants over time to address two key questions: (1) how has the research community responded to the emergence of new variants and (2) how has that response changed over time? We extracted research related to variants in the Research Library using the query ‘variant OR lineage’, allowing us to query metadata from 16 sources of different research types simultaneously (Fig. 2a ). Over 10,000 separate entries about variants are within the Library as of October 2022, including publications, datasets, clinical trials, protocols and more. Using filters and the quality metrics provided through Altmetric badges, we quickly identified which results have been recognized by the community via Altmetric scores, such as a quantitative PCR protocol with reverse transcription (RT–qPCR) to screen VOCs (Fig. 2b ). Clearly, variants are an active area of research, but has this enthusiasm changed over time? Using the outbreak.info R package, we accessed the harmonized metadata to examine the proportion of research related to variants in the Research Library over time. We observed an increase in research on variants following the first identification of VOCs such as Alpha (B.1.1.7*) and Beta (B.1.351*) (Fig. 2c ). This increase was even more prominent for the Omicron (B.1.1.529*) variant in late 2021; we hypothesize that this increase was due to the heightened awareness of the value in studying variants among the scientific community, and early indications that the variant could be of global concern (high growth rate of Omicron and the presence of many mutations in important sites). To examine how research differed by VOC over time, we constructed queries for each VOC, including its Pango lineage name and associated sublineages. With the three VOCs that became the dominant worldwide form of SARS-CoV-2 (Alpha, Delta and Omicron), we find that the increase in research on these VOCs mirrors the rise in worldwide prevalence for each variant, with the research output roughly proportional to global prevalence (Fig. 2d ). With Alpha and Delta, there was a slight lag in research publications that was not observed with Omicron, and research on Omicron over the last 10 months has dwarfed that for the other VOCs. Lastly, research on previously circulating variants (Alpha, Beta, Gamma, Delta) continues, even though these variants are rarely detected presently, and focuses on retrospective analyses, fundamental studies on mechanisms of action, Omicron comparisons and studies of recombinant variants. In sum, the research community’s response to the emergence of new variants has been robust, has become a greater focus of overall research effort over the last year and quickly pivots to studying the dominant variant.

a , Standardized searching across resource types, including Publications, Datasets, ClinicalTrials and more. b , A variant protocol discovered within the Library. c , As VOCs were designated, the proportion of research in the Library focused on variants increased. d , The increase in research on each VOC mirrored its worldwide prevalence, with research on the transmissibility, virulence and/or immune evasion supporting their VOC designation by public health agencies, and these designations encouraging further research.

The outbreak.info Research Library and resources API have been widely used by the external community, including journalists, members of the medical and public health communities, students and biomedical researchers 25 . For instance, the RADx-Rad Data Coordination Center created the SearchOutbreak app ( https://searchoutbreak.netlify.app ), which uses the Outbreak API to collect articles for customized research digests for its partners 26 . On average, the Research Library receives nearly 3,000 pageviews per month, of which 85% are unique visitors (Supplementary Table 1b ). The Research Library site has been used for over 11,000 unique searches, and the Research Library API receives an average of nearly 63,000 unique hits per month (including web traffic and programmatic access). Some limitations of the Research Library include incomplete or unstructured metadata descriptions provided by the sources and optimally querying these descriptions, which often include acronyms and synonyms. Future work will focus on augmenting the harvested metadata and optimizing search results to provide the most salient results to users.

While the unprecedented amount of research on COVID-19 offers new opportunities to accelerate the pace of research, the difficulty in finding research amid this ‘infodemic’ remains a fundamental challenge. In the outbreak.info Research Library, we address many of these challenges to assemble a collection of heterogeneous research outputs and data from distributed data sources into a searchable platform. Our metadata-processing platform is modular, allowing easy extension to add new metadata sources including contributions from the community, allowing the Research Library to grow with the pandemic as research changes. To enable further analysis, we enable programmatic access to the standardized library. Lastly, with the embrace of open science stored in decentralized sources, quickly finding information will be critical for the next pandemic. Our approach to unify metadata across repositories will serve as a template for rapidly creating a unified search interface to aggregate research outputs for any pathogen or any research domain.

Schema development

The development of the schema for standardizing our collection of resources is as previously described 27 . Briefly, we prioritized six classes of resources that had seen a rapid expansion at the start of the pandemic due to their importance to the research community: publications, datasets, clinical trials, analyses, protocols and computational tools. We identified the most closely related classes from https://schema.org and mapped their properties to available metadata from two to five of the most prolific sources. Additionally, we identified subclasses that were needed to support the aforementioned six classes and standardized the properties within each class. In addition to standardizing ready-to-harvest metadata, we created new properties that would support the linkage, exploration and evaluation of our resources. Our schema was then refined as we iterated through the available metadata when assembling COVID-19 resources. For example, publication providers such as PubMed typically use the ‘author’ property in their metadata, while dataset providers such as Figshare and Zenodo are compliant with the DataCite schema and typically prefer ‘creator’. Although both properties are valid for their respective https://schema.org classes, we normalized our schema to use ‘author’ for all six of our classes (Dataset, ClinicalTrial, Analysis, Protocol, Publication, ComputationalTool), because we expected the volume of publications to dwarf that of all other classes of resources. We added this schema to the Schema Registry of the Data Discovery Engine (DDE) 27 , a project to share and reuse schemas and register datasets according to a particular schema. The Outbreak schema is available at https://discovery.biothings.io/view/outbreak .

Assembly of COVID-19 resources

The resource metadata pipeline for outbreak.info includes two ways to ingest metadata (Supplementary Fig. 7 ). First, metadata can be ingested from other resource repositories or collections using the BioThings SDK 28 data plugins. By leveraging the BioThings SDK, we developed a technology stack that addresses the fragmentation issue by easily integrating metadata from different pre-existing resources. For each resource repository or collection, a parser or data plugin enables automated import and updates from that resource. To import the data, the metadata is harvested from the source using API calls (if available), HTML web scraping or .CSV or .TXT tables of metadata. All structured metadata provided by the sources is compiled and mapped to our schema using custom Python scripts. The harmonized metadata is dumped into a JSON output. Supplementary Fig. 8 shows the completeness of each metadata property within our schema, broken down by resource type (data are provided in Supplementary Table 2 ). Data plugin code for the sources is available at https://github.com/outbreak-info (Code availability).

In the second mechanism, metadata for individually curated resources can be submitted via an online form through the DDE Metadata Registry 27 . To assemble the outbreak.info collection of resources, we collected a list of over a hundred separate resources on COVID-19 and SARS-CoV-2. This list (Supplementary Table 3 ) included generalist open data repositories, biomedical-specific data projects including those recommended by the NIH 29 and the NSF 30 to house open data and individual websites that we came across through search engines and other COVID-19 publications. Prioritizing those resources that had a large number of resources related to COVID-19, we selected an initial set of two to three sources per resource type to import into our collection. Given the lack of widespread repositories for analysis resources, only one source would be included in our initial import (Imperial College London 31 ). An analysis resource is defined as a frequently updated, web-based, data visualization, data interpretation and/or data analysis resource.

Creation of the Research Library API and query interface

To accommodate a large number of heterogeneous data sources, each of which is independently harvested, we used the BioThings SDK framework to combine the data sources into a combined, public searchable index (Supplementary Fig. 7 ). The JSON outputs of our data plugins are ingested by the BioThings framework and merged into an intermediary MongoDB database, and the processed data are indexed in an Elasticsearch index that can be accessed through our public API ( api.outbreak.info ). The BioThings SDK plugin architecture handles errors in individual parsers without affecting the availability of the API itself. Errors thrown by individual parsers may result in a lack of updates of an individual resource until the error is resolved, but the API will serve the latest version of data from the broken parser and up-to-date data from all functional parsers, which will continue to be updated independently. Using the plugin architecture also allows the creation and maintenance of individual resource parsers to be crowdsourced to anyone with basic Python knowledge and a GitHub account. Although resource plugins allow outbreak.info to ingest large amounts of standardized metadata, there are still many individual datasets and research outputs scattered throughout the web that are not located in large repositories. As it is not feasible for one team to locate, identify and collect standardized metadata from these individual datasets and research outputs, we leveraged the DDE 27 to enable crowdsourcing and citizen science participation in the curation of individual resource metadata.

A Tornado server is used to create an API endpoint, api.outbreak.info/resources , that leverages the search capabilities of Elasticsearch to efficiently query data. Within the search results, Elasticsearch sorts them by relevance based on Lucene’s Practical Scoring Function 32 , which prioritizes the query normalization factor, coordination factor, term frequency, inverse document frequency and any custom query-boosting fields selected by the user 33 . To adjust this behavior based on common search patterns, we upweighted queries for which the search term occurs in the name field and/or the name of a clinical trial therapeutic intervention (for example, ‘remdesivir’) with the following parameters: weight of 4 for ‘name’ and 3 for ‘interventions.name’. We continue to monitor common query patterns using our analytics to refine the scoring algorithm to improve the list of results for the user. Within the web interface, the user has the option to sort by the best match-relevance score, update date for the document or alphabetically by name. Within search queries, terms are automatically combined by ‘AND’. For instance, the search ‘long COVID’ will be interpreted as ‘long AND COVID’. This search will find resources containing both terms, although not necessarily together; the Elasticsearch default scoring function will first list resources that contain both words together and that frequently mention the terms. Exact phrases can be explicitly declared by encapsulating the terms in quotes (for example, ‘long COVID’ to search only for the phrase ‘long COVID’). Additionally, terms can be combined by the term ‘OR’ (for example, (Moderna OR Pfizer) AND (‘side effects’ OR ‘adverse effects’)). Further details on advanced searching behavior are provided in our guide to the outbreak.info R package at https://outbreak-info.github.io/R-outbreak-info/articles/researchlibrary.html#some-notes-on-constructing-queries . Further optimization will be the subject of future work, based on continuing analysis of analytic patterns for the most common search queries and filters to promote user-driven design. Additional work will also focus on creating an advanced query builder to make it easier to combine terms by any combination of ‘AND’, ‘OR’ and ‘NOT’ and to help the user search for exact phrases.

To update the API with new data provided by the data sources, the BioThings Hub schedules daily updates to pull data upstream and add them to the existing index. The BioThings Hub independently maintains each data source, enabling independence if an individual data source pipeline breaks, and maintains historical data by default, creating automated backups. The code for the server-side application is available at https://github.com/outbreak-info/outbreak.api ( https://doi.org/10.5281/zenodo.7343503 ).

Outbreak.info Research Library web application and metadata access

The web application was built using Vue.js, a model–view–viewmodel JavaScript framework that enables the two-way binding of user interface elements and the underlying data allowing the user interface to reflect any changes in underlying data and vice versa. The client-side application uses the high-performance API to interactively perform operations on the database. To iteratively improve the interface, we conducted usability studies as described in Supplementary Fig. 2 . The code for the client-side application is available at https://github.com/outbreak-info/outbreak.info ( https://doi.org/10.5281/zenodo.7343497 ). To enable programmatic access to all our harmonized metadata collection, all data are available in our API (api.outbreak.info) and can be accessed through an R package as described by Gangavarapu et al. 24 (package website, https://outbreak-info.github.io/R-outbreak-info/ ; code, https://github.com/outbreak-info/R-outbreak-info , https://doi.org/10.5281/zenodo.7343501 ).

Community curation of resource metadata

Resource plugins such as those used in the assembly of COVID-19 resources do not necessarily have to be built by our own team. We used the BioThings SDK 28 and the DDE 27 so that individual resource collections can be added by writing BioThings plugins that conform to our schema. Expanding available classes of resources can be easily carried out by extending other classes from https://schema.org via the DDE Schema Playground at https://discovery.biothings.io/schema-playground . Community contributions of resource plugins can be carried out via GitHub. In addition to contributing resource plugins for collections or repositories of metadata, users can enter metadata for individual resources via the automatic guides created by the DDE. To investigate potential areas of community contribution, we asked two volunteers to inspect 30 individual datasets sprinkled around the web and collect the metadata for these datasets. We compared the results between the two volunteers, and their combined results were subsequently submitted into the collection via the DDE’s Outbreak Data Portal Guide at https://discovery.biothings.io/guide/outbreak/dataset . Although limited by the original submission form (Google forms), the raw and merged responses illustrating the thoroughness of the submissions from the two volunteers can be found at https://docs.google.com/spreadsheets/d/1q1c400UFIOyXedFf2L81zROVkXi3BWBhU46Ic0cMYsI/edit?usp=sharing . Although both of our volunteers provided values for many of the available metadata properties (name, description, topicCategories, keywords, etc.), one provided an extensive list of authors. Using the BioThings SDK in conjunction with the DDE allows us to centralize and leverage individualized curation efforts that often occur at the start of a pandemic. Improvements or updates for manually curated metadata can be submitted via GitHub pull requests.

Community curation of searching, linkage and evaluation metadata and scaling with machine learning

In an effort to enable improved searching and filtering, we developed a nested list of thematic or topic-based categories based on an initial list developed by LitCovid 13 with input from the infectious disease research community and volunteer curators. The list consists of 11 broad categories and 24 specific child categories. LitCovid organized publications into eight research areas such as treatments or prevention, but these classifications are not available in the actual metadata records for each publication. To obtain these classifications from LitCovid, subsetted exports of identifiers were downloaded from LitCovid and then mapped to the metadata records from PubMed.

Whenever possible, sources with thematic categories were mapped to our list of categories to develop a training set for basic binary (in-group–out-group) classifications of required metadata fields such as (title, abstract and/or description). If an already curated training set could not be found for a broad category, it would be created using an iterative process involving term–phrase searching on LitCovid, evaluating the specificity of the results, identifying new search terms by keyword frequency and repeating the process. To generate training data for classifying resources into specific topic categories, the results from several approaches were combined. These approaches include direct mapping from LitCovid research areas, keyword mapping from LitCovid, logical mapping from NCT ClinicalTrials metadata, the aforementioned term search iteration and citizen science curation of Zenodo and Figshare datasets. Details on the logical mapping from NCT ClinicalTrials metadata can be found at https://github.com/gtsueng/outbreak_CT_classifier ( https://doi.org/10.5281/zenodo.7442988 ). The keyword mapping from LitCovid can be found at https://github.com/outbreak-info/topic_classifier/tree/main/data/keyword and https://github.com/outbreak-info/topic_classifier/tree/main/data/subtopics/keywords .

While positive categorical data were identified via the aforementioned methods, negative controls were generated by randomly selecting from alternative topics and ensuring no overlap. The categorical data were randomly split into training (80%) and test (20%) sets per test, and five tests were performed per topic by applying out-of-the-box logistic regression and multinomial naive Bayes and random forest algorithms from scikit-learn. These three algorithms were found to perform best on this binary classification task using out-of-the-box tests. Topics were only added to the record if all three methods agreed on the classification. The set size and test results using default tests from scikit-learn for each algorithm for each topic and subtopic for each of the five test runs can be found at https://github.com/outbreak-info/topic_classifier/blob/main/results/in_depth_classifier_test.tsv .

The efforts of our two volunteers suggested that non-experts were capable of thematically categorizing datasets; therefore, we built a simple interface to allow citizen scientists to thematically classify the datasets that were available in our collection at that point in time. Each dataset was assigned up to five topics by at least three different citizen scientists to ensure quality of the results. Citizen scientists were asked to prioritize specific topic categories over broader ones. Ninety citizen scientists recruited via either participation in the Mark2Cure project 34 or a Scripps Research summer program participated in classifying 530 datasets pulled from Figshare and Zenodo, increasing the likelihood of quality submissions and decreasing the likelihood of abuse and false information. The citizen science-curation site was originally hosted at https://curate.outbreak.info . The code for the site can be found at https://github.com/outbreak-info/outbreak.info-resources/tree/master/citsciclassify . The citizen science classifications can be found at https://github.com/outbreak-info/topic_classifier/blob/main/data/subtopics/curated_training_df.pickle . To evaluate the quality of the citizen scientist classifications, we first filtered classifications where at least two or three of three to five curators agreed on the topic category. We then compared the results of their classification with predictions by an out-of-the-box algorithm that was trained on LitCovid-classified abstracts. A total of 186 of 530 classifications did not agree and were manually inspected; only about 10% of the categorization (54) was worse with citizen scientists over the predictions, and, in many cases, the curators provided more precise categorization. Full details of the evaluation are available at https://github.com/gtsueng/curate_outbreak_data ( https://doi.org/10.5281/zenodo.7442949 ). These classifications have been incorporated into the appropriate datasets in our collection and have been used to build our models for topic categorization. Basic in-group–out-group classification models were developed for each category using out-of-the-box logistic regression and multinomial naive Bayes and random forest algorithms available from scikit-learn. The topic classifier can be found at https://github.com/outbreak-info/topic_classifier ( https://doi.org/10.5281/zenodo.7439573 ).

In addition to community curation of topic categorizations, we identified a citizen science effort, the COVID-19 Literature Surveillance Team (COVID-19 LST), that was evaluating the quality of COVID-19 related literature. The COVID-19 LST consists of medical students (many of which were in their third or fourth year), practitioners and researchers who evaluate publications on COVID-19 based on the Oxford Levels of Evidence criteria and write bottom line, up front summaries 20 . With their permission, we integrated their outputs (daily reports or summaries and Levels of Evidence evaluations) into our collection. Although the project has since ended, the valuable work by this team was integrated without further evaluation due to their background and training.

We further integrated our publications by adding structured linkage metadata, connecting preprints and their peer-reviewed versions. We performed separate Jaccard’s similarity calculations on the title and/or text and authors for preprint (bioRxiv or medRxiv) 35 versus LitCovid publications. We identified thresholds with high precision and low sensitivity and binned the matches into two groups: matched preprint or peer-reviewed publication versus ‘needs review’. We also leveraged NLM’s pilot preprint program to identify and incorporate additional matches. The code used for the preprint matching and the .XLSX file detailing the semi-automated and manual inspection of a sample of 1,500 matches from the results can be found at https://github.com/outbreak-info/outbreak_preprint_matcher ( https://doi.org/10.5281/zenodo.7439581 ). Briefly, a subsample of 1,500 preprint or peer-reviewed matches were inspected and confirmed to match via the preprint listed within the PubMed record in the correction field (1,158 matches); manual inspection of preprint records, which listed the peer-reviewed publication (290 matches); and manual inspection of preprint and the corresponding PubMed record and publication content (52 matches). The inspection confirmed that our threshold cutoff for preprint matching ensured the inclusion of a limited number of the most accurate matches at the cost of many more potential but lower-quality matches. Expected matches were linked via the correction property in our schema.

Case study on variant research

To identify research about variants, we used the keyword phrase ‘variant OR lineage’ in the Research Library and within the R package outbreakinfo. For Fig. 2a , resources were counted by @type (Publication, Dataset, ComputationalTool, ClinicalTrial, Protocol, Analysis). The number of resources was aggregated to the weekly level by the date of the latest update and normalized to all resources within the Library for that week, creating a proportion of the Library for that week (Fig. 2c ). For variant-specific queries, the WHO-designated name was combined with its Pango lineage 36 plus all descendants, as specified by the Pango team in October 2022 ( https://raw.githubusercontent.com/cov-lineages/lineages-website/master/data/lineages.yml ). To decrease the likelihood of a spurious hit for the resource (for instance, a publication mentioning Alpha in the description but focusing only on Omicron), we used fielded queries to only search by the name of the resource. For instance, for Gamma, the following query was used: name:Gamma OR name:‘P.1’ OR name:‘P.1.2’. Code to replicate the analysis and visualizations is available at https://github.com/outbreak-info/outbreak-resources-paper/blob/main/Figure%204%20-%20Variant%20analysis.R .

Harmonization and integration of resources and genomic data

The integration of genomic data from GISAID is discussed by Gangavarapu et al. 24 . We built separate API endpoints for our resources (metadata resource API) and genomics (genomic data API) using the BioThings SDK 28 . Data are available via our API at http://api.outbreak.info and through our R package as described by Gangavarapu et al. 24 .

Limitations

While we have developed a framework for addressing resource volume, fragmentation and variety that can be applicable to future pandemics, our efforts during this framework exposed additional limitations in how data and metadata are currently collected and shared. Researchers have embraced preprints, but resources (especially datasets and computational tools) needed to replicate and extend research results are not linked in ways that are discoverable. Although many journals and funders have embraced dataset and source code submission requirements, the result is that the publication of datasets and software code is still heavily based in publications instead of in community repositories with well-described metadata to promote discoverability and reuse. In the outbreak.info Research Library, the largest research output by far is publications, while dataset submission lags in standardized repositories encouraged by the NIH such as ImmPort, Figshare and Zenodo. We hypothesize that this disparity between preprint and data sharing reflects the existing incentive structure, in which researchers are rewarded for writing papers and less for providing good, reusable datasets. Ongoing efforts to improve metadata standardization and encourage schema adoption (such as the efforts in the Bioschemas community) will help make resources more discoverable in the future, provided researchers adopt and use them. For this uptake to happen, fundamental changes in the incentive structure for sharing research outputs may be necessary. As with many web-based, open-source resource sites, bugs and browser-compatibility issues may arise without notice for less-popular browsers. Users can bring these issues to our attention by submitting them to our issue tracker on GitHub ( https://github.com/outbreak-info/outbreak.info/issues ).

Comparison of the outbreak.info Research Library with other resources

To illustrate how our resource fits into the COVID-19 resource landscape, we compare features from our Research Library with other COVID-19 multisource aggregation efforts (Supplementary Table 4 ) and provide a list of terms and features in Supplementary Table 5 . We provide the most commonly searched sources (that is, filter by source) and resource types (that is, filter by resource type) (Supplementary Table 1a ). Usage statistics for record views and filtering by source are available in Supplementary Table 1b . Filtering was the most popular feature added to the Library, with over a quarter of all queries using some sort of filtering (Supplementary Table 1c ). Users were most likely to filter results by resource type, followed by keywords and source.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All metadata harvested and harmonized in the outbreak.info Research Library is freely available through an API ( http://api.outbreak.info/ ) and in an associated R package ( https://outbreak-info.github.io/R-outbreak-info/ ).

Code availability

All code used to generate the outbreak.info Research Library is freely available on GitHub ( https://github.com/outbreak-info ) under open-source licenses. The outbreak.info web application is available at https://github.com/outbreak-info/outbreak.info (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7343497 ). The outbreak.info R package to access all the genomics and epidemiology data and Research Library metadata compiled and standardized on outbreak.info is available at https://github.com/outbreak-info/R-outbreak-info (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7343501 ). The code to create the API ( https://api.outbreak.info ) to access Research Library metadata and case and death data is available at https://github.com/outbreak-info/outbreak.api (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7343503 ). The harvester of bioRxiv and medRxiv preprint publications is available at https://github.com/outbreak-info/biorxiv (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439483 ). The harvester of clinical trials from https://clinicaltrials.gov is available at https://github.com/outbreak-info/clinical_trials (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439505 ). The harvester of COVID-19 LST level of evidence ratings is available at https://github.com/outbreak-info/covid19_LST_reports (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439527 ). The COVID-19 LST annotations code is available at https://github.com/outbreak-info/covid19_LST_annotations (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439515 ).The COVID-19 LST report data are available at https://github.com/outbreak-info/covid19_LST_report_data (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439521 ). The harvester for manually curated metadata from the DDE is available at https://github.com/biothings/discovery-app/blob/master/scripts/outbreak.py ) (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439590 ). The harvester from Figshare COVID-19 is available at https://github.com/outbreak-info/covid_figshare (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439543 ). The harvester for COVID-19 collection of Harvard Dataverse is available at https://github.com/outbreak-info/dataverses (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439563 ). The harvester for analyses by Imperial College London is available at https://github.com/outbreak-info/covid_imperial_college (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439545 ). The LitCovid publication harvester is available at https://github.com/outbreak-info/litcovid (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439565 ). The harvester of metadata for SARS-CoV-2 structures from the Protein Data Bank is available at https://github.com/outbreak-info/covid_pdb_datasets (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439549 ). The harvester of protocol metadata from protocols.io is available at https://github.com/outbreak-info/protocolsio (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439579 ). The harvester of clinical trials from WHO ICTR is available at https://github.com/outbreak-info/covid_who_clinical_trials/blob/master/parser.py (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439553 ). The reusable Research Library schemas for publications, datasets, clinical trials, protocols and analyses and associated data mappings are available at https://github.com/outbreak-info/outbreak.info-resources (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439569 ). The reusable Research Library tools for parsers are available at https://github.com/outbreak-info/outbreak_parser_tools (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439577 ). The code to look up Altmetric ratings for outbreak.info resources is available at https://github.com/outbreak-info/covid_altmetrics (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439533 ). The code to match preprints to their peer-reviewed publications is available at https://github.com/outbreak-info/outbreak_preprint_matcher (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439581 ). The machine learning topic classification of categories within the Research Library is available at https://github.com/outbreak-info/topic_classifier (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439573 ). The mapping logic used to classify clinical trial records using clinical trial-specific metadata is available at https://github.com/gtsueng/outbreak_CT_classifier (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7442988 ). The evaluation of citizen scientist efforts is available at https://github.com/gtsueng/curate_outbreak_data (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7442949 ). The code to generate the figures within this text, including for the case study, is available at https://github.com/outbreak-info/outbreak-resources-paper (version of the code used in this paper is available at https://doi.org/10.5281/zenodo.7439567 ).

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Acknowledgements

We thank J. Rah, B.J. Enright, J. Doroshenko, T. Nishath and the rest of the COVID-19 LST for allowing us to share their work. We thank T. Adams and C. Lazarchick for their work in identifying metadata from various individual datasets and their extensive feedback. We thank S. Andarmani for her suggestions and feedback on dataset categories. We thank all Outbreak Curators contributors found at https://blog.outbreak.info/dataset-topic-category-contributors for taking the time to categorize datasets. We thank S. Ul-Hasan for their feedback on the R package. We thank D. Valentine for sharing details about his netlify app as part of the RADx-Rad Data Coordination Center, which is funded by the NIH (U24LM013755). Work on outbreak.info was supported by the National Institute for Allergy and Infectious Diseases (5 U19 AI135995: G.T., J.L.M., M.A., M.C., E. Haag, A.A.L., E. Hufbauer, M.Z., K.G.A., C.W., A.I.S., K.G., L.D.H.; 3 U19 AI135995-04S3: G.T., J.L.M., E. Haag, E. Hufbauer, K.G.A., C.W., A.I.S., K.G., L.D.H.; 3 U19 AI135995-03S2: G.T., J.L.M., E. Haag, E. Hufbauer, K.G.A., C.W., A.I.S., K.G., L.D.H.; 75N91019D00024: G.T., E. Haag, J.L., D.J.W., C.W., A.I.S., L.D.H.), the National Center for Advancing Translational Sciences (5 U24 TR002306: G.T., J.L.M., M.C., C.W., A.I.S., L.D.H.), the Centers for Disease Control and Prevention (75D30120C09795: M.A., A.A.L., M.Z., K.G.A., K.G.) and the National Institute of General Medical Sciences (R01GM083924: G.T., M.C., X.Z., Z.Q., C.W., A.I.S.).

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Ginger Tsueng, Julia L. Mullen, Marco Cano, Emily Haag, Jason Lin, Dylan J. Welzel, Xinghua Zhou, Zhongchao Qian, Chunlei Wu, Andrew I. Su & Laura D. Hughes

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L.D.H., K.G., M.C., E. Haag, J.L.M., X.Z., Z.Q., E. Hufbauer, C.W., A.I.S., K.G.A., A.A.L., M.Z., G.T., J.L. and D.J.W. contributed to the design, construction and/or maintenance of the outbreak.info website and data pipelines. K.G., M.A. and L.D.H. designed and built the R outbreak.info package. M.A., A.A.L., K.G., E. Haag, E. Hufbauer, M.Z., K.G.A. and L.D.H. designed and linked the variant reports. L.D.H., J.L.M., G.T. and M.C. developed the schemas. E. Haag performed the usability studies. B.R. developed the curation app. L.D.H., G.T., E. Haag, K.G., M.Z. and J.L.M. contributed to writing and editing the manuscript.

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Tsueng, G., Mullen, J.L., Alkuzweny, M. et al. Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources. Nat Methods 20 , 536–540 (2023). https://doi.org/10.1038/s41592-023-01770-w

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Brief Review on COVID-19: The 2020 Pandemic Caused by SARS-CoV-2

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• 1 Internal Medicine, Kettering Medical Center, Dayton, USA.
• PMID: 32337113
• PMCID: PMC7179986
• DOI: 10.7759/cureus.7386

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the coronavirus disease of 2019 (COVID-19). First identified in Wuhan (Hubei, China) in December of 2019, it has since been declared a pandemic by the World Health Organization in March of 2020. In this study, we will provide a brief review of viral origin, identification, symptoms, transmission, diagnosis, and potential treatment strategies for the newly identified SARS-CoV-2 strain.

Keywords: 2019-ncov; acei; arb; chloroquine; corona virus; corticosteroids; covid-19; novel coronavirus; remdesivir; sars-cov-2.

Copyright © 2020, Valencia et al.

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Figure 1. SARS-CoV

Electron microscopy image of…

Electron microscopy image of SARS-CoV, with the arrow pointing at a single…

Figure 2. Genomes and structures for SARS-CoV…

Figure 2. Genomes and structures for SARS-CoV and MERS-CoV

The image shows the key SARS-CoV…

Figure 3. Replication cycle of SARS-CoV and…

Figure 3. Replication cycle of SARS-CoV and MERS-CoV

This image details the replication cycle of…

Figure 4. Electron microscopy image of SARS-CoV-2…

Figure 4. Electron microscopy image of SARS-CoV-2 virions

Electron microscopy image of SARS-CoV-2, with the…

Figure 5. CT of the chest in…

Figure 5. CT of the chest in a COVID-19 patient

Axial CT of the chest…

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COVID-19, also called coronavirus disease 2019, is an illness caused by a virus. The virus is called severe acute respiratory syndrome coronavirus 2, or more commonly, SARS-CoV-2. It started spreading at the end of 2019 and became a pandemic disease in 2020.

• Coronavirus

Coronaviruses are a family of viruses. These viruses cause illnesses such as the common cold, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and coronavirus disease 2019 (COVID-19).

The virus that causes COVID-19 spreads most commonly through the air in tiny droplets of fluid between people in close contact. Many people with COVID-19 have no symptoms or mild illness. But for older adults and people with certain medical conditions, COVID-19 can lead to the need for care in the hospital or death.

Staying up to date on your COVID-19 vaccine helps prevent serious illness, the need for hospital care due to COVID-19 and death from COVID-19 . Other ways that may help prevent the spread of this coronavirus includes good indoor air flow, physical distancing, wearing a mask in the right setting and good hygiene.

Medicine can limit the seriousness of the viral infection. Most people recover without long-term effects, but some people have symptoms that continue for months.

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Typical COVID-19 symptoms often show up 2 to 14 days after contact with the virus.

Symptoms can include:

• Shortness of breath.
• Loss of taste or smell.
• Extreme tiredness, called fatigue.
• Digestive symptoms such as upset stomach, vomiting or loose stools, called diarrhea.
• Pain, such as headaches and body or muscle aches.
• Fever or chills.
• Cold-like symptoms such as congestion, runny nose or sore throat.

People may only have a few symptoms or none. People who have no symptoms but test positive for COVID-19 are called asymptomatic. For example, many children who test positive don't have symptoms of COVID-19 illness. People who go on to have symptoms are considered presymptomatic. Both groups can still spread COVID-19 to others.

Some people may have symptoms that get worse about 7 to 14 days after symptoms start.

Most people with COVID-19 have mild to moderate symptoms. But COVID-19 can cause serious medical complications and lead to death. Older adults or people who already have medical conditions are at greater risk of serious illness.

COVID-19 may be a mild, moderate, severe or critical illness.

• In broad terms, mild COVID-19 doesn't affect the ability of the lungs to get oxygen to the body.
• In moderate COVID-19 illness, the lungs also work properly but there are signs that the infection is deep in the lungs.
• Severe COVID-19 means that the lungs don't work correctly, and the person needs oxygen and other medical help in the hospital.
• Critical COVID-19 illness means the lung and breathing system, called the respiratory system, has failed and there is damage throughout the body.

Rarely, people who catch the coronavirus can develop a group of symptoms linked to inflamed organs or tissues. The illness is called multisystem inflammatory syndrome. When children have this illness, it is called multisystem inflammatory syndrome in children, shortened to MIS -C. In adults, the name is MIS -A.

When to see a doctor

Contact a healthcare professional if you test positive for COVID-19 . If you have symptoms and need to test for COVID-19 , or you've been exposed to someone with COVID-19 , a healthcare professional can help.

People who are at high risk of serious illness may get medicine to block the spread of the COVID-19 virus in the body. Or your healthcare team may plan regular checks to monitor your health.

Get emergency help right away for any of these symptoms:

• Can't catch your breath or have problems breathing.
• Skin, lips or nail beds that are pale, gray or blue.
• New confusion.
• Trouble staying awake or waking up.
• Chest pain or pressure that is constant.

This list doesn't include every emergency symptom. If you or a person you're taking care of has symptoms that worry you, get help. Let the healthcare team know about a positive test for COVID-19 or symptoms of the illness.

More Information

• COVID-19 vs. flu: Similarities and differences
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• Unusual symptoms of coronavirus

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COVID-19 is caused by infection with the severe acute respiratory syndrome coronavirus 2, also called SARS-CoV-2.

The coronavirus spreads mainly from person to person, even from someone who is infected but has no symptoms. When people with COVID-19 cough, sneeze, breathe, sing or talk, their breath may be infected with the COVID-19 virus.

The coronavirus carried by a person's breath can land directly on the face of a nearby person, after a sneeze or cough, for example. The droplets or particles the infected person breathes out could possibly be breathed in by other people if they are close together or in areas with low air flow. And a person may touch a surface that has respiratory droplets and then touch their face with hands that have the coronavirus on them.

It's possible to get COVID-19 more than once.

• Over time, the body's defense against the COVID-19 virus can fade.
• A person may be exposed to so much of the virus that it breaks through their immune defense.
• As a virus infects a group of people, the virus copies itself. During this process, the genetic code can randomly change in each copy. The changes are called mutations. If the coronavirus that causes COVID-19 changes in ways that make previous infections or vaccination less effective at preventing infection, people can get sick again.

The virus that causes COVID-19 can infect some pets. Cats, dogs, hamsters and ferrets have caught this coronavirus and had symptoms. It's rare for a person to get COVID-19 from a pet.

Risk factors

The main risk factors for COVID-19 are:

• If someone you live with has COVID-19 .
• If you spend time in places with poor air flow and a higher number of people when the virus is spreading.
• If you spend more than 30 minutes in close contact with someone who has COVID-19 .

Many factors affect your risk of catching the virus that causes COVID-19 . How long you are in contact, if the space has good air flow and your activities all affect the risk. Also, if you or others wear masks, if someone has COVID-19 symptoms and how close you are affects your risk. Close contact includes sitting and talking next to one another, for example, or sharing a car or bedroom.

It seems to be rare for people to catch the virus that causes COVID-19 from an infected surface. While the virus is shed in waste, called stool, COVID-19 infection from places such as a public bathroom is not common.

Serious COVID-19 illness risk factors

Some people are at a higher risk of serious COVID-19 illness than others. This includes people age 65 and older as well as babies younger than 6 months. Those age groups have the highest risk of needing hospital care for COVID-19 .

Not every risk factor for serious COVID-19 illness is known. People of all ages who have no other medical issues have needed hospital care for COVID-19 .

Known risk factors for serious illness include people who have not gotten a COVID-19 vaccine. Serious illness also is a higher risk for people who have:

• Sickle cell disease or thalassemia.
• Serious heart diseases and possibly high blood pressure.
• Chronic kidney, liver or lung diseases.

People with dementia or Alzheimer's also are at higher risk, as are people with brain and nervous system conditions such as stroke. Smoking increases the risk of serious COVID-19 illness. And people with a body mass index in the overweight category or obese category may have a higher risk as well.

Other medical conditions that may raise the risk of serious illness from COVID-19 include:

• Cancer or a history of cancer.
• Type 1 or type 2 diabetes.
• Weakened immune system from solid organ transplants or bone marrow transplants, some medicines, or HIV .

This list is not complete. Factors linked to a health issue may raise the risk of serious COVID-19 illness too. Examples are a medical condition where people live in a group home, or lack of access to medical care. Also, people with more than one health issue, or people of older age who also have health issues have a higher chance of severe illness.

Related information

• COVID-19: Who's at higher risk of serious symptoms? - Related information COVID-19: Who's at higher risk of serious symptoms?

Complications

Complications of COVID-19 include long-term loss of taste and smell, skin rashes, and sores. The illness can cause trouble breathing or pneumonia. Medical issues a person already manages may get worse.

Complications of severe COVID-19 illness can include:

• Acute respiratory distress syndrome, when the body's organs do not get enough oxygen.
• Shock caused by the infection or heart problems.
• Overreaction of the immune system, called the inflammatory response.
• Blood clots.
• Kidney injury.

Post-COVID-19 syndrome

After a COVID-19 infection, some people report that symptoms continue for months, or they develop new symptoms. This syndrome has often been called long COVID, or post- COVID-19 . You might hear it called long haul COVID-19 , post-COVID conditions or PASC. That's short for post-acute sequelae of SARS -CoV-2.

Other infections, such as the flu and polio, can lead to long-term illness. But the virus that causes COVID-19 has only been studied since it began to spread in 2019. So, research into the specific effects of long-term COVID-19 symptoms continues.

Researchers do think that post- COVID-19 syndrome can happen after an illness of any severity.

Getting a COVID-19 vaccine may help prevent post- COVID-19 syndrome.

• Long-term effects of COVID-19

The Centers for Disease Control and Prevention (CDC) recommends a COVID-19 vaccine for everyone age 6 months and older. The COVID-19 vaccine can lower the risk of death or serious illness caused by COVID-19.

The COVID-19 vaccines available in the United States are:

2023-2024 Pfizer-BioNTech COVID-19 vaccine. This vaccine is available for people age 6 months and older.

Among people with a typical immune system:

• Children age 6 months up to age 4 years are up to date after three doses of a Pfizer-BioNTech COVID-19 vaccine.
• People age 5 and older are up to date after one Pfizer-BioNTech COVID-19 vaccine.
• For people who have not had a 2023-2024 COVID-19 vaccination, the CDC recommends getting an additional shot of that updated vaccine.

2023-2024 Moderna COVID-19 vaccine. This vaccine is available for people age 6 months and older.

• Children ages 6 months up to age 4 are up to date if they've had two doses of a Moderna COVID-19 vaccine.
• People age 5 and older are up to date with one Moderna COVID-19 vaccine.

2023-2024 Novavax COVID-19 vaccine. This vaccine is available for people age 12 years and older.

• People age 12 years and older are up to date if they've had two doses of a Novavax COVID-19 vaccine.

In general, people age 5 and older with typical immune systems can get any vaccine approved or authorized for their age. They usually don't need to get the same vaccine each time.

Some people should get all their vaccine doses from the same vaccine maker, including:

• Children ages 6 months to 4 years.
• People age 5 years and older with weakened immune systems.
• People age 12 and older who have had one shot of the Novavax vaccine should get the second Novavax shot in the two-dose series.

Talk to your healthcare professional if you have any questions about the vaccines for you or your child. Your healthcare team can help you if:

• The vaccine you or your child got earlier isn't available.
• You don't know which vaccine you or your child received.
• You or your child started a vaccine series but couldn't finish it due to side effects.

People with weakened immune systems

Your healthcare team may suggest added doses of COVID-19 vaccine if you have a moderately or seriously weakened immune system. The FDA has also authorized the monoclonal antibody pemivibart (Pemgarda) to prevent COVID-19 in some people with weakened immune systems.

Control the spread of infection

In addition to vaccination, there are other ways to stop the spread of the virus that causes COVID-19 .

If you are at a higher risk of serious illness, talk to your healthcare professional about how best to protect yourself. Know what to do if you get sick so you can quickly start treatment.

If you feel ill or have COVID-19 , stay home and away from others, including pets, if possible. Avoid sharing household items such as dishes or towels if you're sick.

In general, make it a habit to:

• Test for COVID-19 . If you have symptoms of COVID-19 test for the infection. Or test five days after you came in contact with the virus.
• Help from afar. Avoid close contact with anyone who is sick or has symptoms, if possible.
• Wash your hands. Wash your hands well and often with soap and water for at least 20 seconds. Or use an alcohol-based hand sanitizer with at least 60% alcohol.
• Cover your coughs and sneezes. Cough or sneeze into a tissue or your elbow. Then wash your hands.
• Clean and disinfect high-touch surfaces. For example, clean doorknobs, light switches, electronics and counters regularly.

Try to spread out in crowded public areas, especially in places with poor airflow. This is important if you have a higher risk of serious illness.

The CDC recommends that people wear a mask in indoor public spaces if you're in an area with a high number of people with COVID-19 in the hospital. They suggest wearing the most protective mask possible that you'll wear regularly, that fits well and is comfortable.

• COVID-19 vaccines: Get the facts - Related information COVID-19 vaccines: Get the facts
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Travel and COVID-19

Travel brings people together from areas where illnesses may be at higher levels. Masks can help slow the spread of respiratory diseases in general, including COVID-19 . Masks help the most in places with low air flow and where you are in close contact with other people. Also, masks can help if the places you travel to or through have a high level of illness.

Masking is especially important if you or a companion have a high risk of serious illness from COVID-19 .

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• Goldman L, et al., eds. COVID-19: Epidemiology, clinical manifestations, diagnosis, community prevention, and prognosis. In: Goldman-Cecil Medicine. 27th ed. Elsevier; 2024. https://www.clinicalkey.com. Accessed Dec. 17, 2023.
• Coronavirus disease 2019 (COVID-19) treatment guidelines. National Institutes of Health. https://www.covid19treatmentguidelines.nih.gov/. Accessed Dec. 18, 2023.
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• Long COVID or post-COVID conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html. Accessed Jan. 10, 2024.
• Stay up to date with your vaccines. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html. Accessed Jan. 10, 2024.
• Interim clinical considerations for use of COVID-19 vaccines currently approved or authorized in the United States. Centers for Disease Control and Prevention. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html#CoV-19-vaccination. Accessed Jan. 10, 2024.
• Use and care of masks. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/about-face-coverings.html. Accessed Jan. 10, 2024.
• How to protect yourself and others. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html. Accessed Jan. 10, 2024.
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• Masking during travel. Centers for Disease Control and Prevention. https://wwwnc.cdc.gov/travel/page/masks. Accessed Jan. 10, 2024.
• AskMayoExpert. COVID-19: Testing. Mayo Clinic. 2023.
• COVID-19 test basics. U.S. Food and Drug Administration. https://www.fda.gov/consumers/consumer-updates/covid-19-test-basics. Accessed Jan. 11, 2024.
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• AskMayoExpert. COVID:19 Drug regimens and other treatment options. Mayo Clinic. 2023.
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Coronavirus disease 2019 (COVID-19): A literature review

Harapan harapan.

a Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

b Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

c Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

d Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan

Amanda Yufika

e Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

Wira Winardi

f Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

g School of Medicine, The University of Western Australia, Perth, Australia

Haypheng Te

h Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia

Dewi Megawati

i Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia

j Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA

Zinatul Hayati

k Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

Abram L. Wagner

l Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA

Mudatsir Mudatsir

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

On December 31, 2019, the China Health Authority alerted the World Health Organization (WHO) to several cases of pneumonia of unknown aetiology in Wuhan City in Hubei Province in central China. The cases had been reported since December 8, 2019, and many patients worked at or lived around the local Huanan Seafood Wholesale Market although other early cases had no exposure to this market [1] . On January 7, a novel coronavirus, originally abbreviated as 2019-nCoV by WHO, was identified from the throat swab sample of a patient [2] . This pathogen was later renamed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the Coronavirus Study Group [3] and the disease was named coronavirus disease 2019 (COVID-19) by the WHO. As of January 30, 7736 confirmed and 12,167 suspected cases had been reported in China and 82 confirmed cases had been detected in 18 other countries [4] . In the same day, WHO declared the SARS-CoV-2 outbreak as a Public Health Emergency of International Concern (PHEIC) [4] .

According to the National Health Commission of China, the mortality rate among confirmed cased in China was 2.1% as of February 4 [5] and the mortality rate was 0.2% among cases outside China [6] . Among patients admitted to hospitals, the mortality rate ranged between 11% and 15% [7] , [8] . COVID-19 is moderately infectious with a relatively high mortality rate, but the information available in public reports and published literature is rapidly increasing. The aim of this review is to summarize the current understanding of COVID-19 including causative agent, pathogenesis of the disease, diagnosis and treatment of the cases, as well as control and prevention strategies.

The virus: classification and origin

SARS-CoV-2 is a member of the family Coronaviridae and order Nidovirales. The family consists of two subfamilies, Coronavirinae and Torovirinae and members of the subfamily Coronavirinae are subdivided into four genera: (a) Alphacoronavirus contains the human coronavirus (HCoV)-229E and HCoV-NL63; (b) Betacoronavirus includes HCoV-OC43, Severe Acute Respiratory Syndrome human coronavirus (SARS-HCoV), HCoV-HKU1, and Middle Eastern respiratory syndrome coronavirus (MERS-CoV); (c) Gammacoronavirus includes viruses of whales and birds and; (d) Deltacoronavirus includes viruses isolated from pigs and birds [9] . SARS-CoV-2 belongs to Betacoronavirus together with two highly pathogenic viruses, SARS-CoV and MERS-CoV. SARS-CoV-2 is an enveloped and positive-sense single-stranded RNA (+ssRNA) virus [16] .

SARS-CoV-2 is considered a novel human-infecting Betacoronavirus [10] . Phylogenetic analysis of the SARS-CoV-2 genome indicates that the virus is closely related (with 88% identity) to two bat-derived SARS-like coronaviruses collected in 2018 in eastern China (bat-SL-CoVZC45 and bat-SL-CoVZXC21) and genetically distinct from SARS-CoV (with about 79% similarity) and MERS-CoV [10] . Using the genome sequences of SARS-CoV-2, RaTG13, and SARS-CoV [11] , a further study found that the virus is more related to BatCoV RaTG13, a bat coronavirus that was previously detected in Rhinolophus affinis from Yunnan Province, with 96.2% overall genome sequence identity [11] . A study found that no evidence of recombination events detected in the genome of SARS-CoV-2 from other viruses originating from bats such as BatCoV RaTG13, SARS-CoV and SARSr-CoVs [11] . Altogether, these findings suggest that bats might be the original host of this virus [10] , [11] .

However, a study is needed to elucidate whether any intermediate hosts have facilitated the transmission of the virus to humans. Bats are unlikely to be the animal that is directly responsible for transmission of the virus to humans for several reasons [10] : (1) there were various non-aquatic animals (including mammals) available for purchase in Huanan Seafood Wholesale Market but no bats were sold or found; (2) SARS-CoV-2 and its close relatives, bat-SL-CoVZC45 and bat-SL-CoVZXC21, have a relatively long branch (sequence identity of less than 90%), suggesting those viruses are not direct ancestors of SARS-CoV-2; and (3) in other coronaviruses where bat is the natural reservoir such as SARS-CoV and MERS-CoV, other animals have acted as the intermediate host (civets and possibly camels, respectively). Nevertheless, bats do not always need an intermediary host to transmit viruses to humans. For example, Nipah virus in Bangladesh is transmitted through bats shedding into raw date palm sap [12] .

Transmission

The role of the Huanan Seafood Wholesale Market in propagating disease is unclear. Many initial COVID-19 cases were linked to this market suggesting that SARS-CoV-2 was transmitted from animals to humans [13] . However, a genomic study has provided evidence that the virus was introduced from another, yet unknown location, into the market where it spread more rapidly, although human-to-human transmission may have occurred earlier [14] . Clusters of infected family members and medical workers have confirmed the presence of person-to-person transmission [15] . After January 1, less than 10% of patients had market exposure and more than 70% patients had no exposure to the market [13] . Person-to-person transmission is thought to occur among close contacts mainly via respiratory droplets produced when an infected person coughs or sneezes. Fomites may be a large source of transmission, as SARS-CoV has been found to persist on surfaces up to 96 h [16] and other coronaviruses for up to 9 days [17] .

Whether or not there is asymptomatic transmission of disease is controversial. One initial study published on January 30 reported asymptomatic transmission [18] , but later it was found that the researchers had not directly interviewed the patient, who did in fact have symptoms prior to transmitting disease [19] . A more recent study published on February 21 also purported asymptomatic transmission [20] , but any such study could be limited by errors in self-reported symptoms or contact with other cases and fomites.

Findings about disease characteristics are rapidly changing and subject to selection bias. A study indicated the mean incubation period was 5.2 days (95% confidence interval [95%CI]: 4.1–7.0) [13] . The incubation period has been found to be as long as 19 or 24 days [21] , [22] , although case definitions typically rely on a 14 day window [23] .

The basic reproductive number ( R 0 ) has been estimated with varying results and interpretations. R 0 measures the average number of infections that could result from one infected individual in a fully susceptible population [24] . Studies from previous outbreaks found R 0 to be 2.7 for SARS [25] and 2.4 for 2009 pandemic H1N1 influenza [26] . One study estimated that that basic reproductive number ( R 0 ) was 2.2 (95% CI: 1.4–3.9) [13] . However, later in a further analysis of 12 available studies found that R 0 was 3.28 [27] . Because R 0 represents an average value it is also important to consider the role of super spreaders, who may be hugely responsible for outbreaks within large clusters but who would not largely influence the value of R 0 [28] . During the acute phase of an outbreak or prepandemic, R 0 may be unstable [24] .

In pregnancy, a study of nine pregnancy women who developed COVID-19 in late pregnancy suggested COVID-19 did not lead to substantially worse symptoms than in nonpregnant persons and there is no evidence for intrauterine infection caused by vertical transmission [29] .

In hospital setting, a study involving 138 COVID-19 suggested that hospital-associated transmission of SARS-CoV-2 occurred in 41% of patients [30] . Moreover, another study on 425 patients found that the proportion of cases in health care workers gradually increased by time [13] . These cases likely reflect exposure to a higher concentration of virus from sustained contact in close quarters.

Outside China, as of February 12, 2020, there were 441 confirmed COVID-19 cases reported in 24 countries [6] of which the first imported case was reported in Thailand on January 13, 2020 [6] , [31] . Among those countries, 11 countries have reported local transmission with the highest number of cases reported in Singapore with 47 confirmed cases [6] .

Risk factors

The incidence of SARS-CoV-2 infection is seen most often in adult male patients with the median age of the patients was between 34 and 59 years [20] , [30] , [7] , [32] . SARS-CoV-2 is also more likely to infect people with chronic comorbidities such as cardiovascular and cerebrovascular diseases and diabetes [8] . The highest proportion of severe cases occurs in adults ≥60 years of age, and in those with certain underlying conditions, such as cardiovascular and cerebrovascular diseases and diabetes [20] , [30] . Severe manifestations maybe also associated with coinfections of bacteria and fungi [8] .

Fewer COVID-19 cases have been reported in children less than 15 years [20] , [30] , [7] , [32] . In a study of 425 COVID-19 patients in Wuhan, published on January 29, there were no cases in children under 15 years of age [13] , [33] . Nevertheless, 28 paediatric patients have been reported by January 2020 [34] . The clinical features of infected paediatric patients vary, but most have had mild symptoms with no fever or pneumonia, and have a good prognosis [34] . Another study found that although a child had radiological ground-glass lung opacities, the patient was asymptomatic [35] . In summary, children might be less likely to be infected or, if infected, present milder manifestations than adults; therefore, it is possible that their parents will not seek out treatment leading to underestimates of COVID-19 incidence in this age group.

Pathogenesis and immune response

Like most other members of the coronavirus family, Betacoronavirus exhibit high species specificity, but subtle genetic changes can significantly alter their tissue tropism, host range, and pathogenicity. A striking example of the adaptability of these viruses is the emergence of deadly zoonotic diseases in human history caused by SARS-CoV [36] and MERS-CoV [37] . In both viruses, bats served as the natural reservoir and humans were the terminal host, with the palm civet and dromedary camel the intermediary host for SARS-CoV and MERS-CoV, respectively [38] , [39] . Intermediate hosts clearly play a critical role in cross species transmission as they can facilitate increased contact between a virus and a new host and enable further adaptation necessary for an effective replication in the new host [40] . Because of the pandemic potential of SARS-CoV-2, careful surveillance is immensely important to monitor its future host adaptation, viral evolution, infectivity, transmissibility, and pathogenicity.

The host range of a virus is governed by multiple molecular interactions, including receptor interaction. The envelope spike (S) protein receptor binding domain of SARS-CoV-2 was shown structurally similar to that of SARS-CoV, despite amino acid variation at some key residues [10] . Further extensive structural analysis strongly suggests that SARS-CoV-2 may use host receptor angiotensin-converting enzyme 2 (ACE2) to enter the cells [41] , the same receptor facilitating SARS-CoV to infect the airway epithelium and alveolar type 2 (AT2) pneumocytes, pulmonary cells that synthesize pulmonary surfactant [42] . In general, the spike protein of coronavirus is divided into the S1 and S2 domain, in which S1 is responsible for receptor binding and S2 domain is responsible for cell membrane fusion [10] . The S1 domain of SARS-CoV and SARS-CoV-2 share around 50 conserved amino acids, whereas most of the bat-derived viruses showed more variation [10] . In addition, identification of several key residues (Gln493 and Asn501) that govern the binding of SARS-CoV-2 receptor binding domain with ACE2 further support that SARS-CoV-2 has acquired capacity for person-to-person transmission [41] . Although, the spike protein sequence of receptor binding SARS-CoV-2 is more similar to that of SARS-CoV, at the whole genome level SARS-CoV-2 is more closely related to bat-SL-CoVZC45 and bat-SL-CoVZXC21 [10] .

However, receptor recognition is not the only determinant of species specificity. Immediately after binding to their receptive receptor, SARS-CoV-2 enters host cells where they encounter the innate immune response. In order to productively infect the new host, SARS-CoV-2 must be able to inhibit or evade host innate immune signalling. However, it is largely unknown how SARS-CoV-2 manages to evade immune response and drive pathogenesis. Given that COVID-19 and SARS have similar clinical features [7] , SARS-CoV-2 may have a similar pathogenesis mechanism as SARS-CoV. In response to SARS-CoV infections, the type I interferon (IFN) system induces the expression of IFN-stimulated genes (ISGs) to inhibit viral replication. To overcome this antiviral activity, SARS-CoV encodes at least 8 viral antagonists that modulate induction of IFN and cytokines and evade ISG effector function [43] .

The host immune system response to viral infection by mediating inflammation and cellular antiviral activity is critical to inhibit viral replication and dissemination. However, excessive immune responses together with lytic effects of the virus on host cells will result in pathogenesis. Studies have shown patients suffering from severe pneumonia, with fever and dry cough as common symptoms at onset of illness [7] , [8] . Some patients progressed rapidly with Acute Respiratory Stress Syndrome (ARDS) and septic shock, which was eventually followed by multiple organ failure and about 10% of patients have died [8] . ARDS progression and extensive lung damage in COVID-19 are further indications that ACE2 might be a route of entry for the SARS-CoV-2 as ACE2 is known abundantly present on ciliated cells of the airway epithelium and alveolar type II (cells (pulmonary cells that synthesize pulmonary surfactant) in humans [44] .

Patients with SARS and COVID-19 have similar patterns of inflammatory damage. In serum from patients diagnosed with SARS, there is increased levels of proinflammatory cytokines (e.g. interleukin (IL)-1, IL6, IL12, interferon gamma (IFNγ), IFN-γ-induced protein 10 (IP10), macrophage inflammatory proteins 1A (MIP1A) and monocyte chemoattractant protein-1 (MCP1)), which are associated with pulmonary inflammation and severe lung damage [45] . Likewise, patients infected with SARS-CoV-2 are reported to have higher plasma levels of proinflammatory cytokines including IL1β, IL-2, IL7, TNF-α, GSCF, MCP1 than healthy adults [7] . Importantly, patients in the intensive care unit (ICU) have a significantly higher level of GSCF, IP10, MCP1, and TNF-α than those non-ICU patients, suggesting that a cytokine storm might be an underlying cause of disease severity [7] . Unexpectedly, anti-inflammatory cytokines such as IL10 and IL4 were also increased in those patients [7] , which was uncommon phenomenon for an acute phase viral infection. Another interesting finding, as explained before, was that SARS-CoV-2 has shown to preferentially infect older adult males with rare cases reported in children [7] , [8] . The same trend was observed in primate models of SARS-CoV where the virus was found more likely to infect aged Cynomolgus macaque than young adults [46] . Further studies are necessary to identify the virulence factors and the host genes of SARS-CoV-2 that allows the virus to cross the species-specific barrier and cause lethal disease in humans.

Clinical manifestations

Clinical manifestations of 2019-nCoV infection have similarities with SARS-CoV where the most common symptoms include fever, dry cough, dyspnoea, chest pain, fatigue and myalgia [7] , [30] , [47] . Less common symptoms include headache, dizziness, abdominal pain, diarrhoea, nausea, and vomiting [7] , [30] . Based on the report of the first 425 confirmed cases in Wuhan, the common symptoms include fever, dry cough, myalgia and fatigue with less common are sputum production, headache, haemoptysis, abdominal pain, and diarrhoea [13] . Approximately 75% patients had bilateral pneumonia [8] . Different from SARS-CoV and MERS-CoV infections, however, is that very few COVID-19 patients show prominent upper respiratory tract signs and symptoms such as rhinorrhoea, sneezing, or sore throat, suggesting that the virus might have greater preference for infecting the lower respiratory tract [7] . Pregnant and non-pregnant women have similar characteristics [48] . The common clinical presentation of 2019-nCoV infection are presented in Table 1 .

Clinical symptoms of patients with 2019-nCoV infection.

Severe complications such as hypoxaemia, acute ARDS, arrythmia, shock, acute cardiac injury, and acute kidney injury have been reported among COVID-19 patients [7] , [8] . A study among 99 patients found that approximately 17% patients developed ARDS and, among them, 11% died of multiple organ failure [8] . The median duration from first symptoms to ARDS was 8 days [30] .

Efforts to control spread of COVID-19, institute quarantine and isolation measures, and appropriately clinically manage patients all require useful screening and diagnostic tools. While SARS-CoV-2 is spreading, other respiratory infections may be more common in a local community. The WHO has released a guideline on case surveillance of COVID-19 on January 31, 2020 [23] . For a person who meets certain criteria, WHO recommends to first screen for more common causes of respiratory illness given the season and location. If a negative result is found, the sample should be sent to referral laboratory for SARS-CoV-2 detection.

Case definitions can vary by country and will evolve over time as the epidemiological circumstances change in a given location. In China, a confirmed case from January 15, 2020 required an epidemiological linkage to Wuhan within 2 weeks and clinical features such as fever, pneumonia, and low white blood cell count. On January 18, 2020 the epidemiological criterion was expanded to include contact with anyone who had been in Wuhan in the past 2 weeks [50] . Later, the case definitions removed the epidemiological linkage.

The WHO has put forward case definitions [23] . Suspected cases of COVID-19 are persons (a) with severe acute respiratory infections (history of fever and cough requiring admission to hospital) and with no other aetiology that fully explains the clinical presentation and a history of travel to or residence in China during the 14 days prior to symptom onset; or (b) a patient with any acute respiratory illness and at least one of the following during the 14 days prior to symptom onset: contact with a confirmed or probable case of SARS-CoV-2 infection or worked in or attended a health care facility where patients with confirmed or probable SARS-CoV-2 acute respiratory disease patients were being treated. Probable cases are those for whom testing for SARS-CoV-2 is inconclusive or who test positive using a pan-coronavirus assay and without laboratory evidence of other respiratory pathogens. A confirmed case is one with a laboratory confirmation of SARS-CoV-2 infection, irrespective of clinical signs and symptoms.

For patients who meet diagnostic criteria for SARS-CoV-2 testing, the CDC recommends collection of specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swab) and, if possible, the lower respiratory tract (sputum, tracheal aspirate, or bronchoalveolar lavage) [51] . In each country, the tests are performed by laboratories designated by the government.

Laboratory findings

Among COVID-19 patients, common laboratory abnormalities include lymphopenia [8] , [20] , [30] , prolonged prothrombin time, and elevated lactate dehydrogenase [30] . ICU-admitted patients had more laboratory abnormalities compared with non-ICU patients [30] , [7] . Some patients had elevated aspartate aminotransferase, creatine kinase, creatinine, and C-reactive protein [20] , [7] , [35] . Most patients have shown normal serum procalcitonin levels [20] , [30] , [7] .

COVID-19 patients have high level of IL1β, IFN-γ, IP10, and MCP1 [7] . ICU-admitted patients tend to have higher concentration of granulocyte-colony stimulating factor (GCSF), IP10, MCP1A, MIP1A, and TNF-α [7] .

Radiology findings

Radiology finding may vary with patients age, disease progression, immunity status, comorbidity, and initial medical intervention [52] . In a study describing 41 of the initial cases of 2019-nCoV infection, all 41 patients had pneumonia with abnormal findings on chest computed tomography (CT-scan) [7] . Abnormalities on chest CT-scan were also seen in another study of 6 cases, in which all of them showed multifocal patchy ground-glass opacities notably nearby the peripheral sections of the lungs [35] . Data from studies indicate that the typical of chest CT-scan findings are bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities [7] , [8] , [20] , [30] , [32] , [53] . The consolidated lung lesions among patients five or more days from disease onset and those 50 years old or older compared to 4 or fewer days and those 50 years or younger, respectively [47] .

As the disease course continue, mild to moderate progression of disease were noted in some cases which manifested by extension and increasing density of lung opacities [49] . Bilateral multiple lobular and subsegmental areas of consolidation are typical findings on chest CT-scan of ICU-admitted patients [7] . A study among 99 patients, one patient had pneumothorax in an imaging examination [8] .

Similar to MERS-CoV and SARS-CoV, there is still no specific antiviral treatment for COVID-19 [54] . Isolation and supportive care including oxygen therapy, fluid management, and antibiotics treatment for secondary bacterial infections is recommended [55] . Some COVID-19 patients progressed rapidly to ARDS and septic shock, which was eventually followed by multiple organ failure [7] , [8] . Therefore, the effort on initial management of COVID-19 must be addressed to the early recognition of the suspect and contain the disease spread by immediate isolation and infection control measures [56] .

Currently, no vaccination is available, but even if one was available, uptake might be suboptimal. A study of intention to vaccinate during the H1N1 pandemic in the United States was around 50% at the start of the pandemic in May 2009 but had decreased to 16% by January 2010 [57] .

Neither is a treatment available. Therefore, the management of the disease has been mostly supportive referring to the disease severity which has been introduced by WHO. If sepsis is identified, empiric antibiotic should be administered based on clinical diagnosis and local epidemiology and susceptibility information. Routine glucocorticoids administration are not recommended to use unless there are another indication [58] . Clinical evidence also does not support corticosteroid treatment [59] . Use of intravenous immunoglobulin might help for severely ill patients [8] .

Drugs are being evaluated in line with past investigations into therapeutic treatments for SARS and MERS [60] . Overall, there is not robust evidence that these antivirals can significantly improve clinical outcomes A. Antiviral drugs such as oseltamivir combined with empirical antibiotic treatment have also been used to treat COVID-19 patients [7] . Remdesivir which was developed for Ebola virus, has been used to treat imported COVID-19 cases in US [61] . A brief report of treatment combination of Lopinavir/Ritonavir, Arbidol, and Shufeng Jiedu Capsule (SFJDC), a traditional Chinese medicine, showed a clinical benefit to three of four COVID-19 patients [62] . There is an ongoing clinical trial evaluating the safety and efficacy of lopinavir-ritonavir and interferon-α 2b in patients with COVID-19 [55] . Ramsedivir, a broad spectrum antivirus has demonstrated in vitro and in vivo efficacy against SARS-CoV-2 and has also initiated its clinical trial [63] , [64] . In addition, other potential drugs from existing antiviral agent have also been proposed [65] , [66] .

Control and prevention strategies

COVID-19 is clearly a serious disease of international concern. By some estimates it has a higher reproductive number than SARS [27] , and more people have been reported to have been infected or died from it than SARS [67] . Similar to SARS-CoV and MERS-CoV, disrupting the chain of transmission is considered key to stopping the spread of disease [68] . Different strategies should be implemented in health care settings and at the local and global levels.

Health care settings can unfortunately be an important source of viral transmission. As shown in the model for SARS, applying triage, following correct infection control measures, isolating the cases and contact tracing are key to limit the further spreading of the virus in clinics and hospitals [68] . Suspected cases presenting at healthcare facilities with symptoms of respiratory infections (e.g. runny nose, fever and cough) must wear a face mask to contain the virus and strictly adhere triage procedure. They should not be permitted to wait with other patients seeking medical care at the facilities. They should be placed in a separated, fully ventilated room and approximately 2 m away from other patients with convenient access to respiratory hygiene supplies [69] . In addition, if a confirmed COVID-19 case require hospitalization, they must be placed in a single patient room with negative air pressure – a minimum of six air changes per hour. Exhausted air has to be filtered through high efficiency particulate air (HEPA) and medical personnel entering the room should wear personal protective equipment (PPE) such as gloves, gown, disposable N95, and eye protection. Once the cases are recovered and discharged, the room should be decontaminated or disinfected and personnel entering the room need to wear PPE particularly facemask, gown, eye protection [69] .

In a community setting, isolating infected people are the primary measure to interrupt the transmission. For example, immediate actions taken by Chinese health authorities included isolating the infected people and quarantining of suspected people and their close contacts [70] . Also, as there are still conflicting assumptions regarding the animal origins of the virus (i.e. some studies linked the virus to bat [71] , [72] while others associated the virus with snake [73] ), contacts with these animal fluids or tissues or consumption of wild caught animal meet should be avoided. Moreover, educating the public to recognize unusual symptoms such as chronic cough or shortness of breath is essential therefore that they could seek medical care for early detection of the virus. If large-scale community transmission occurs, mitigating social gatherings, temporary school closure, home isolation, close monitoring of symptomatic individual, provision of life supports (e.g. oxygen supply, mechanical ventilator), personal hand hygiene, and wearing personal protective equipment such as facemask should also be enforced [74] .

In global setting, locking down Wuhan city was one of the immediate measure taken by Chinese authorities and hence had slowed the global spread of COVID-19 [74] . Air travel should be limited for the cases unless severe medical attentions are required. Setting up temperature check or scanning is mandatory at airport and border to identify the suspected cases. Continued research into the virus is critical to trace the source of the outbreak and provide evidence for future outbreak [74] .

Conclusions

The current COVID-19 pandemic is clearly an international public health problem. There have been rapid advances in what we know about the pathogen, how it infects cells and causes disease, and clinical characteristics of disease. Due to rapid transmission, countries around the world should increase attention into disease surveillance systems and scale up country readiness and response operations including establishing rapid response teams and improving the capacity of the national laboratory system.

Competing interests

The authors declare that they have no competing interests.

Ethical approval

Not required.