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Coronavirus Disease (COVID-19) – Statistics and Research

Note: To inform yourself and understand the risk to the public we recommend to rely on your government body responsible for health and the World Health Organization – their site is here.


The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.

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While most of our work focuses on large problems that humanity has faced for a long time – such as child mortality, natural disasters, poverty and almost 100 other problems (see here) – this article focuses on a new, emerging global problem: the ongoing outbreak of the coronavirus disease [COVID-19].

The outbreak started in late 2019 and developed into a global pandemic by March 2020.


This article covers a developing situation and the Our World in Data team is regularly updating it: The last update was made on March 19, 2020 (15:30 London time).

About this page

Limitations of current research and limitations of our presentation of current research

The purpose of this article on COVID-19 is to aggregate existing research, bring together the relevant data and allow readers to make sense of the published data and early research on the coronavirus outbreak.

Most of our work focuses on established problems, for which we can refer to well-established research and data. COVID-19 is different. All data and research on the virus is preliminary; researchers are rapidly learning more about a new and evolving problem. It is certain that the research we present here will be revised in the future. But based on our mission we feel it is our role to present clearly what the current research and data tells us about this emerging problem and especially to provide an understanding of what can and cannot be said based on this available knowledge.

As always in our work, one important strategy of dealing with this problem is to always link to the underlying original research and data so that everyone can understand how this data was produced and how we arrive at the statements we make. But scrutiny of all reported research and data is very much required. We welcome your feedback. In the current situation we read and consider all feedback, but can not promise to reply to all.

Our World in Data relies on data from the European CDC

In this document and the associated charts we report and visualize the data from the European Center for Disease Control and Prevention (ECDC). Established in 2005 and based in Stockholm it is an EU agency with the aim to strengthen Europe’s defense against infectious diseases.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world.

The European CDC collects and aggregates data from countries around the world. The most up-to-date data for any particular country is therefore typically earlier available via the national health agencies than via the ECDC.

This lag between nationally available data and the ECDC data is not very long as the ECDC publishes new data daily. But it can be several hours.

We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries. The European CDC data provides a global perspective on the evolving pandemic.

The ECDC makes all their data available in a daily updated clean downloadable file. This gets updated daily reflecting data collected up to 6:00 and 10:00 CET. The data made public via the downloadable data file is published at 1pm CET, and is used to produce a page that gets updated daily under the name Situation Update Worldwide.

Why we stopped relying on data from the World Health Organization

Until March 18 we relied on the World Health Organization (WHO) as our source. We aimed to rely on the WHO as they are the international agency with the mandate to provide official estimates on the pandemic. The WHO reports this data for each single day and they can be found here at the WHO’s site.

Since March 18 it became unfortunately impossible to rely on the WHO data to understand how the pandemic is developing over time. With Situation Report 58 the WHO shifted the reporting cutoff time from 0900 CET to 0000 CET. This means that comparability is compromised because there is an overlap between these two WHO data publications (Situation Reports 57 and 58).

Additionally we found many errors in the data published by the WHO when we went through all the daily Situation Reports. We immediately notified the WHO and are in close contact with the WHO’s team to correct the errors that we pointed out to them. We document all errors we found. The main problem we see with the WHO data is that these errors are not communicated by the WHO itself (some Errata were published by the WHO – in the same place as the Situation Reports –, but most errors were either retrospectively corrected without public notice or remain uncorrected).

Here is our detailed documentation of where the WHO’s data is sourced from and how we corrected its data – we also provide several options to download all corrected data there. As of March 18 we no longer maintain this database for the reason that the WHO data can not be used for reliable time-series information.

Other data sources on the COVID-19 pandemic

A number of other organizations – including Johns Hopkins University and other research teams – publish their own lists of the number of confirmed cases and deaths. Johns Hopkins also publishes data on ‘recovered cases’ while the WHO and the ECDC do not.

At the end of this page we link to their visualizations and list links to other data sources.

Deaths from COVID-19

Confirmed deaths to date is what we know

What we know is the total number of confirmed deaths to date.

The European Center for Disease Control and Prevention (ECDC) publishes daily updates of confirmed deaths due to COVID-19. We rely on this data as explained above.

Based on the ECDC data we can track how the number of deaths has changed over time.

In an ongoing outbreak the final outcomes – death or recovery – for all cases is not yet known. The time from symptom onset to death ranges from 2 to 8 weeks for COVID-19.1 This means that some people who are currently infected with COVID-19 will die at a later date. As we explain below, this needs to be kept in mind when comparing the current number of deaths with the current number of cases.

What does the data on deaths and cases tell us about the mortality risk of COVID-19?

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 – the likelihood that someone who catches the disease will die from it.

We will look into this question in more detail below and explain that this requires us to know (or estimate) the number of total cases and the final number of deaths for a given infected population. Because these are not known, we discuss what the current data can and can not tell us about the risk of death (scroll there).

The growth rate of COVID-19 deaths

How long did it take for the number of confirmed deaths to double?

In the section below we present the latest data on the number of confirmed deaths by country.

But in an outbreak of an infectious disease it is important to not only study the number of deaths, but also the growth rate at which the number of deaths is increasing.

This is because even if the current numbers of deaths are small when compared with other diseases, a fast growth rate can lead to very large numbers rapidly.

To report the rate of change we focus on the question: How long did it take for the number of confirmed deaths to double?

Let’s take an example: if the number of confirmed deaths as of today is 1000, and there were only 500 deaths three days ago then we would say that it took three days for the number of confirmed deaths to double.2

The doubling time of deaths has changed and it will change in the future. It would be wrong to extrapolate current growth into the future.3

It is important to understand what it means for deaths to double. As long as deaths are doubling at a constant rate the growth is exponential. We humans tend to think in linear growth processes even when the growth is exponential, as psychological research has shown for decades. Below we give some intuition about exponential growth and provide the referenced psychological research on this.

Understanding exponential growth

It is helpful to remind ourselves of the nature of exponential growth.

If during an outbreak the number of deaths is in fact doubling and this doubling time stays constant, then the outbreak is spreading exponentially.

Under exponential growth 500 deaths grow to more than 1 million deaths after 11 doubling times.4 And after 10 more doubling times it would be 1 billion deaths.

This is in no way a prediction for the number of deaths we should expect; it is a reminder that exponential growth leads to very large numbers very quickly, even when starting from a low base.

It is important to be reminded of the nature of exponential growth because most of us do not grasp exponential growth intuitively. Psychologists find that humans tend to think in linear growth processes (1, 2, 3, 4) even when this is not appropriately describing the reality in front of our eyes. This bias – to “linearize exponential functions when assessing them intuitively” – is referred to as exponential growth bias.5

Psychological research also shows that “neither special instructions about the nature of exponential growth nor daily experience with growth processes” improved the failure to grasp exponential growth processes.6

The global average hides more than it reveals: why we show this data country by country

Some countries – like China and Korea – have very substantial counter measures in place and new daily confirmed deaths have declined (see the chart here).

Many other countries do not have comparable measures in place and, as the table below shows, numbers are quickly rising.

Because of these large differences between countries it is crucial to not only study the global situation, but the situation in each country. The global average hides these differences.

Growth: Country by country view

For these reasons the following table focuses on the following question for all countries: How long did it take for the number of confirmed deaths to double?

The table also shows how the total number of confirmed deaths, and the number of daily new confirmed deaths has changed over the last 14 days.

You can sort the table by any of the columns by clicking on the column header.

Data: The data shown here is published by the European Center for Disease Control and Prevention (ECDC). Here is our documentation of the data and an option to download all data.

The figures shown are based on the ECDC data up to and 19 March 2020 (10:00 Central European Time).

Confirmed COVID-19 deaths by country

In our visualizations here you can explore the number of total deaths and daily new deaths for all countries with reported deaths. This is shown in absolute numbers, and adjusted for population size by showing total and new deaths per million people.

These charts are interactive: the data is shown as the worldwide figures by default but can be explored by country – by clicking on + Add Country within the chart.

The data shown here is published by the European Center for Disease Control and Prevention (ECDC). Here is the documentation of the data and an option to download all data.

Deaths per million were calculated by Our World in Data based by diving these death numbers from the European Center for Disease Control and Prevention (ECDC) by population figures published in the United Nations’ World Population Prospects.

Testing for COVID-19

To understand the pandemic and respond appropriately we would want to know the total number of people infected with COVID-19. To know how many are infected we need to test for COVID-19.

Why is testing important?

Testing allows infected people to know that they are infected. This can help them receive the care they need; and it can help them take measures to reduce the probability of infecting others. People who don’t know they are infected might not stay at home and thereby risk infecting others.

Testing is also crucial for an appropriate response to the pandemic. It allows us to understand the spread of the disease and to take evidence-based measures to slow down the spread of the disease.7

Unfortunately, the capacity for COVID-19 testing is still low in many countries around the world. For this reason we still do not have a good understanding of the spread of the pandemic.

How are COVID-19 tests done?

The most common diagnostic tests for COVID-19 are the so-called “PCR tests”. These tests rely on swabbed samples from a patient’s nose and throat.

The first PCR tests were developed within two weeks of the disease being identified, and are currently part of the protocol recommended by the WHO.8

Here you can find an explainer video on how the tests for coronavirus disease work.

What information about test coverage do we currently have?

Ideally, we would want to know how many people in the world are being tested for COVID-19 every day, what the results of these tests are, and how the available tests are being allocated.

Unfortunately there is no centralized database by the WHO on COVID-19 testing.

Several countries however do publish the relevant statistics on the total number of tests performed. These reports are published across individual websites, statistical reports and press releases – often in multiple languages and updated with different periodicity.

Because a global overview was not available, we at Our World in Data brought together a large number of data sources from individual national reports.

Below we show the most recent data as of 17 March 2020, 18.30 GMT. This requires a lot of careful work, but we will do our best to expand and update these estimates regularly.9

Current COVID-19 test coverage estimates

Total tests by country

The two charts here show the most recent official estimates of tests we have been able to find as of 17 March 2020, 18.30 GMT. Note that the estimates refer to different dates for each country.

The first chart plots the total number of tests against the total number of confirmed cases.

Since cases can only be confirmed with the tests, this chart shows a positive correlation: Countries with higher confirmed cases tend to be countries where more tests have been conducted.

Importantly, however, there are still large differences between countries, even for similar levels of confirmed cases. The UK has for example done many more tests than other European countries with a similar number of confirmed cases.

The second chart plots the number of tests, country by country.

There are too many countries in our dataset by now. For this reason not all countries with available data are shown by default – you can select the option ‘add country’ to see the available estimates for other countries.

The available data shows that South Korea has done many more tests than other countries. This suggests that the number of confirmed cases in Korea is closer to the total number of cases than in other countries.

It is therefore particularly encouraging to see that the number of daily confirmed cases in South Korea has decreased. (Here you find our chart that shows the decline of confirmed new cases in South Korea.)

The fact that South Korea was able to expand testing so quickly shows that it is possible.

Because testing is crucial it is important that in the coming days other countries follow.

[NB. We provide two estimates for the US. The estimate labelled “US – CDC samples tested” is from the Centers for Disease Control and Prevention, and refers to the number of tests conducted, not the number of individuals tested. The estimates labeled “United States” correspond to estimates of people tested, according to data gathered by the COVID Tracking Project – these estimates are updated more frequently.”]

Per capita tests by country

The two charts here show the most recent official estimates of tests we have been able to find as of 17 March 2020, 18.30 GMT. Note that the estimates refer to different dates for each country.

The first chart takes the size of the population into account. It plots the total number of tests per million people, against the total number of confirmed cases, also per million people.

We see that countries with higher rates of confirmed cases tend to be also countries where a larger share of the total population has been tested.

But again there are important differences between countries. Vietnam, for example, shows a much lower testing rate than Russia, although at this point in time (17 March 2020) both have a similar number of confirmed cases per million people

From this perspective, it is clear that the US is lagging behind. Per capita the number of tests in the US is almost 10-times lower than in Canada and more than 40-times lower than in South Korea. The US has had big problems rolling out their testing strategy.

The second chart plots the number of tests, country by country. Not all countries with available data are shown by default – you can select the option ‘add country’ to see the available estimates for other countries.

Country by country estimates and sources

We list estimates country by country, including exact dates and links to the underlying source, in a companion page here.

The number of COVID-19 tests does not reflect the number of people who have a definitive diagnosis

Some people require more than one test because of false-negative outcomes

Whilst the number of COVID-19 tests completed will be relatively similar to the number of people being tested for the disease, it is not the case these numbers should be expected to be the same. 

This is because some people may need to be tested multiple times. The reason for this is that there are “false-negative” test outcomes.10 1112,13 

False-negative outcomes correspond to people who initially receive a negative test result, but who are later found to have the disease upon re-testing. The World Health Organization (WHO), in its guidelines for laboratory testing of COVID-19, states that “one or more negative results do not rule out the possibility of COVID-19 virus infection.”14 

An important implication from false-negative tests is that even in countries with extensive tests coverage, it is still difficult to know the true total number of COVID-19 cases. In other words: false-negative tests may contribute to widen the gap between the true total number of cases, and the known number of confirmed cases.

Based on the limited number of studies on the prevalence of false-negative outcomes to date, it’s still difficult to put a definitive number on how common they are. It is an active area of scientific research.15

Why might COVID-19 tests fail?

There are several reasons why someone infected with COVID-19 may produce a false-negative result when tested:1617

  • They may be in the early stage of the disease with a viral load that is too low to be detected.
  • They may have no major respiratory symptoms, so there could be little detectable virus in the patient’s throat and nose.
  • There may have been a problem with sample collection, meaning there was very little sample to test.
  • There may have been poor handling and shipping of samples and test materials.
  • There may have been technical issues inherent in the test, e.g. virus mutation.

The WHO notes that a negative result can be found from an infected individual for the reasons listed above, and advises that depending on the specific situation of each patient, additional tests should be collected and tested.18

Cases of COVID-19

The number of total cases is what we want to know, but their number is not known

To understand the scale of the COVID-19 outbreak, and respond appropriately, we would want to know how many people are infected by COVID-19. We would want to know the total number of cases.

However, the total number of COVID-19 cases is not known. When media outlets claim to report the ‘number of cases’ they are not being precise and omit to say that it is the number of confirmed cases they speak about.

The total number of cases is not known, not by us at Our World in Data, nor by any other research, governmental or reporting institution.

Confirmed cases is what we do know

What we do know is the number of confirmed cases.

A confirmed case is “a person with laboratory confirmation of COVID-19 infection” as World Health Organization (WHO) explains.19 But specifics can differ and the European CDC, on which we rely, reports confirmed cases according to the applied case definition in the countries.20

What is important however is that the number of confirmed cases is certainly not the same as the number of total cases. Confirmed cases are therefore only a subset of the total number of cases. It is a count of only those people who have COVID-19 and for whom a lab has confirmed this diagnosis. For this reason we emphasized the importance of testing in the section before.

The total number of confirmed cases is of course also not the same as the total number of all current cases. This is because for some of them the disease has ended and they have either recovered or died from it. We discuss how long the disease lasts further below (scroll there).

Why is the number of confirmed cases lower than the number of total cases?

The number of confirmed cases is lower than the number of total cases because not everyone is tested. Not all cases have a “laboratory confirmation”, testing is what makes the difference between the number of confirmed and total cases.

All countries have been struggling to test a large number of cases, which meant that not every person that should have been tested, has in fact been tested.

Since an understanding of testing for COVID-19 is crucial for an interpretation of the reported numbers of confirmed cases we have looked into the testing for COVID-19 in more detail.

You find our work on testing further below in this document (click here to scroll there).

Growth of cases: How long did it take for the number of confirmed cases to double?

As for the number of deaths, it is not only important to study the number of cases, but also how they increase over time. Their growth rate.

To report the rate of change we focus on the question: How long did it take for the number of confirmed cases to double?

The global average hides more than it reveals: why we show this data country by country

Some countries – like China and Korea – have very substantial counter measures in place and new daily confirmed cases have declined.

Many other countries do not have comparable measures in place and, as the table shows, numbers are rising fast.

Because of these large differences between countries it is crucial to not only study the global situation, but the situation in each country.

The global average hides the differences between countries that are successfully reducing the number of new daily confirmed cases and those that do not achieve this.

Growth: Country by country view

For these reasons the following table answers the following question for all countries: How long did it take for the number of total confirmed cases to double?

The table also shows how the total number of confirmed cases has increased and how the number of daily new confirmed has changed over the last 14 days.

You can sort the table by any of the columns by clicking on the column header.

Data: The data shown here is published by the European Center for Disease Control and Prevention (ECDC). Here is our documentation of the data and an option to download all data.

The figures shown are based on the ECDC data up to and 19 March 2020 (10:00 Central European Time).

Confirmed COVID-19 cases by country

In our visualizations here you can explore the number of total confirmed cases and daily new confirmed cases for all countries with reported cases. This is shown in absolute numbers, and adjusted for population size by showing total and new confirmed cases per million people.

These charts are interactive: the data is shown as the worldwide figures by default but can be explored by country – by clicking on + Add Country within the chart.

Data: The data shown here is published by the European Center for Disease Control and Prevention (ECDC). Here is the documentation of the data and an option to download all data.

Confirmed cases per million were calculated by Our World in Data based by diving these case numbers from the ECDC by population figures published in the United Nations’ World Population Prospects.

Trajectories since the 100th confirmed case

Did the number of confirmed cases rise faster in China, Italy, South Korea, or the US?

The charts above are not very useful to answer these types of questions, because the outbreak of COVID-19 did not happen at the same day in all countries.

The chart shown here is designed to allow these comparisons.

This chart allows the reader to compare the trajectory of confirmed cases between countries. The starting point for each country is the day that particular country had reached 100 confirmed cases.

China had a particular fast rise. Just 10 days after the 100th confirmed case the country already confirmed the 10,000th case.

Other countries saw a much slower increase. The speed at which the number of confirmed cases increased in Singapore and Japan was much slower than in other countries.

The grey lines show trajectories for a doubling time of 2 days and a doubling time of 3 days. Countries that follow a steeper rise have seen a doubling time faster than that.

The trajectory of China and South Korea shows that the speed at which cases rise is not necessarily constant over time. Both countries saw a rapid initial rise but then implemented severe counter measures (see here). As the chart shows the trajectory became flatter, the speed of the outbreak has decreased.

The COVID-19 pandemic

The name of the disease and the virus

The names for the virus and the disease it causes have been announced by the World Health Organization and the International Committee on Taxonomy of Viruses.21

The disease is called coronavirus disease. It is abbreviated as COVID-19.

The virus is called severe acute respiratory syndrome coronavirus 2 and it is abbreviated as SARS-CoV-2. In the same statement the WHO also explains that they themselves also refer to the virus as “the virus responsible for COVID-19” or “the COVID-19 virus” when communicating with the public. We follow the same conventions here.

How did the outbreak start?

On 29 December 2019 Chinese authorities identified a cluster of similar cases of pneumonia in the city of Wuhan in China. Wuhan is a city with 11 million inhabitants and is the capital of the Hubei Province.

These cases were soon determined to be caused by a novel coronavirus that was later named SARS-CoV-2.22

Coronaviruses are a group of viruses that are common in humans and are responsible for up to 30% of common colds.23 Corona is Latin for “crown” – this group of viruses is given its name due to the fact that its surface looks like a crown under an electron microscope.

Two outbreaks of new diseases in recent history were also caused by coronaviruses – SARS in 2003 that resulted in around 1,000 deaths24 and MERS in 2012 that resulted in 862 deaths.25

The first cases of COVID-19 outside of China were identified on January 13 in Thailand and on January 16 in Japan.

On January 23rd the city of Wuhan and other cities in the region were placed on lockdown by the Chinese Government.

Since then COVID-19 has spread to many more countries – cases have been reported in all world regions. You can see the latest available data in the dashboards of cases and deaths which are kept up-to-date by Johns Hopkins University and the WHO discussed here.

  • Pneumonia – Severe cases of COVID-19 can progress to pneumonia.26 Our entry on pneumonia provides an overview of the data and research on this disease that kills 2.6 million annually.
  • Age Structure – Since the mortality risk for COVID-19 varies by age, the age structure of the population matters for the risk that the disease poses to the population. This entry looks in detail at the age structure of countries around the world.

Strategies to respond to COVID-19

The intention of early containment

A lower peak of the outbreak allows the healthcare system to provide care for more people

The total mortality of an epidemic can be high even if the symptoms for the vast majority are mild. While it might not seem intuitive, it is possible for the following two things to be true at the same time:

  • For the majority of people, symptoms are mild and in some cases similar to the common flu.
  • An epidemic of the same disease can cause a very high number of deaths.

As we discuss here, the symptoms of COVID-19 can be very severe in many cases. Many of these patients require treatment in intensive care units (ICUs). The WHO reports that “about a quarter of severe and critical cases require mechanical ventilation.”27

‘Flattening the curve’

This is why early counter measures are important in an epidemic. Their intention is to lower the rate of infection so that the epidemic is spread out over time such that the peak demand on the healthcare system is lower.

Containment measures are intended to avoid an outbreak trajectory in which a large number of people get sick at the same time. This is what the visualization shows.

This is the reason that limiting the magnitude of peak incidence of an outbreak is important. Health systems can care for more patients across an outbreak when the number of cases is spread out over a long period rather than condensed in a very short period.

What such counter measures to the pandemic attempt to avoid is that the number of patients at one point in time is so large that health systems fail to provide the required care for some patients.28

Flattening the curve 3

COVID-19: What are the symptoms? How does the disease progress?

If you suspect that you have COVID-19 please see the official guidance, information and advice provided by the WHO or the health agency of your country (COVID-19 information by the National Health Service of the UK is here; and by the US CDC here). 

Why we need to study data to know the symptoms of COVID-19

COVID-19 leads to a number of symptoms, but from what is known currently some symptoms are much more common than others and for this reason we need to look at the available data.

The danger of relying solely on text and not on numbers is that crucial nuance can get lost. This is the case for the media coverage of the symptoms of COVID-19.

Coverage of the disease, even in reputable sources, includes long lists of symptoms without conveying to the reader how common or rare the listed symptoms are – here is a poor example from the BBC. It is crucial to know how common the various symptoms of COVID-19 are, as it allows a better assessment of whether one suffers from the disease or not. This is lost in reporting that relies on text  – especially if the list of potential symptoms is long, and overlaps strongly with many other types of illness.

In a simple list of COVID-19 symptoms the reader might see that muscle pain is listed as a symptom and then wrongly conclude that they do not have the disease if they are not suffering from muscle pain. Knowing the frequency of symptoms means knowing that the vast majority of known cases (85% in the sample below) did not suffer from this symptom.

The symptoms of COVID-19

The WHO described the symptoms of 55,924 laboratory confirmed cases of COVID-19 in China in the period up to February 20.29

The visualization here shows this data.

It is most crucial to know the common symptoms: fever and a dry cough.

As the visualization shows, close to 90% of cases had a fever and two-thirds had a dry cough.

The third most common symptom was fatigue. Almost 40% of cases suffered from it.

‘Sputum production’ was experienced by every third person. Sputum is not saliva. It is a thick mucus which is coughed up from the lungs (see here). 

Of the 55,924 cases fewer than 1-in-5 (18.6%) experienced shortness of breath (‘dyspnoea’). An earlier study, reported that a much higher share (55%) of cases suffered from dyspnoea, but this was based on a much smaller number of cases (835 patients).30

Many of the most common symptoms are shared with those of the common flu or cold. So it is also good to know which common symptoms of the common flu or the common cold are not symptoms of COVID-19. COVID-19 infection seems to rarely cause a runny nose.

Coronavirus symptoms – who joint mission 2

How long is the incubation period of COVID-19?

The WHO writes “people with COVID-19 generally develop signs and symptoms, including mild respiratory symptoms and fever, on an average of 5-6 days after infection.”31

While the mean incubation period is 5 to 6 days, the WHO adds that the incubation period can vary in a wide range of between 1 to 14 days.32 

This is based on the 55,924 confirmed cases in China. There are reports of cases with longer incubation periods in the media (a case of 27 days is reported here).

How long does COVID-19 last?

On average the disease lasts two weeks. The WHO reports that “the median time from onset to clinical recovery for mild cases is approximately 2 weeks.”33

Again this is based on the 55,924 confirmed cases in China

For severe and critical cases it is 3 to 6 weeks according to the same study.

And for those who eventually died, the time from symptom onset to death ranged from 2 to 8 weeks. This is important when interpreting the case fatality rate (see below). Measures of the CFR of an ongoing outbreak do (obviously) not include deaths of patients who will eventually die, but have not died yet at the time of measurement. This means that the current CFR would be lower than the eventual CFR.

How does COVID-19 progress?

The symptoms of the disease develop and change over time.

It seems to be common that symptoms start with a fever, followed by a dry cough.34

After several days some patients experience shortness of breath.

Symptoms can increase in severity as emphasised in the following section. In severe and critical cases it can lead to severe pneumonia, respiratory failure, septic shock, and multiple organ dysfunction or failure.

As we discuss in detail below, for some cases COVID-19 leads to death.

The severity of the symptoms of COVID-19

This visualization shows the severity of symptoms suffered by 44,415 Chinese patients confirmed to have coronavirus in the early period up to February 11.35

It is likely that many more cases were so mild that they were not identified as COVID-19. Estimates published by Read et al. (2020) suggest that only around 5% of cases in China have been diagnosed and recorded.36

Symptoms were categorized as mild, severe, or critical and the research article describes these as follows:

Critical cases: Critical cases include patients who suffered respiratory failure, septic shock, and/or multiple organ dysfunction or failure.

Severe cases: This includes patients who suffered from shortness of breath, respiratory frequency ≥ 30/minute, blood oxygen saturation ≤93%, PaO2/FiO2 ratio <300,37 and/or lung infiltrates >50% within 24–48 hours. 

Mild cases: The majority (81%) of these coronavirus disease cases were mild cases. Mild cases include all patients without pneumonia or cases of mild pneumonia.

Severity of coronavirus cases in china 1

What do we know about the risk of dying from COVID-19?

What we would like to know is the answer to a straightforward question: How likely is it that a person who is infected by COVID-19 dies because of it? 

A number of metrics allow us to get a perspective on this risk of mortality. However, to make sense of these metrics we need to understand their definitions and the challenges in measuring them. This is the focus of the section below.

The case fatality rate (CFR)

Most current discussions of the mortality risk of COVID-19 focus on the case fatality rate (CFR).

In the worst cases, journalists pretend that the case fatality rate (CFR) gives an answer to the question above. But this is not the case.

While it is a relevant metric, the CFR does not in fact tell us the risk for an infected person to die. 

It is straightforward to understand what the CFR is. The case fatality rate is the share who died from the disease among individuals diagnosed with the disease. 

It is simply the ratio between the number of confirmed deaths from the disease and the number of confirmed cases (not total cases).

\text{ Case Fatality Rate (CFR, in %) }=\frac{\text{ Number of deaths from disease }}{\text{ Number of diagnosed cases of disease }}\times100

The case fatality rate is sometimes called case fatality risk or case fatality ratio. But it is not the same as the crude mortality rate

The crude mortality rate

The crude mortality rate measures the probability that any individual in the population will die from the disease – not just those who are confirmed cases. It is a very different measure. It’s calculated by dividing the number of deaths from the disease by the total population. This crude mortality rate is sometimes also referred to as the crude death rate.

This is important to differentiate, because unfortunately people sometimes confuse case fatality rates with crude death rates. A common example is the Spanish flu pandemic in 1918. The often cited estimate by Johnson and Mueller (2002) is that 50 million people died globally from this pandemic and this implies that 2.7% of the world population at the time died. This means the crude mortality rate was 2.7%. But 2.7% is often misreported as the case fatality rate.38 If it was in fact the case that the crude mortality rate was 2.7% then the case fatality rate was much higher, since not everyone in the world was infected with the Spanish flu. [We look at the global death count of this pandemic and others here.]

Before we consider what the CFR tells us about the mortality risk it is helpful to see what the CFR does not tell us.

We would want to know the infection fatality risk, when we only know the case fatality rate 

It is the ‘infection fatality risk’ (IFR) that would give us the answer to the question: How likely is it that a person who is infected by COVID-19 dies because of it?  

The IFR is the number of deaths from a disease divided by the total number of cases.39,40,41,42,43

To calculate this we need to know two metrics: the total number of cases and the total number of deaths. 

However, as we explained in detail above (scroll there) the total number of cases is not known, one important reason for this is that not everyone with COVID-19 is tested.44,45 

We can possibly estimate the total number of cases and use it to calculate the IFR and researchers do this. 

But the IFR cannot be calculated based on the available data since the total number of cases is not known. And it is wrong if some authors suggest that the CFR is the same – or even similar – to the IFR. Let’s see why.

Interpreting the case fatality rate

It is important to understand the measurement challenges to understand what the case fatality rate can and cannot tell us about a disease outbreak.

There is no single case fatality rate for a disease – it is context-specific, changing with time and location

Unfortunately, it is common to report the CFR as a single value. But the CFR is not a biological constant. The CFR is not a value which is tied to the given disease, but is instead reflective of the severity of the disease in a particular context, at a particular time, in a particular population. 

The probability that someone dies from a disease is not only dependent on the disease itself, but also the social and individual response to it: the level and timing of treatment they receive, and the ability of the given individual to recover from it.

This means that the CFR can decrease or increase over time, and that it can vary by location and by the characteristics of the infected population (age, sex, pre-existing conditions).

The CFR of COVID-19 differs by location and has changed during the early period of the outbreak

In the chart here we see that the case fatality rate of COVID-19 is not constant. This chart was published in the Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), in February 2020.46

The plotted values of the COVID-19 CFR here refer to several locations in China during the early stages of the outbreak, from the beginning of January 2020 to 20th February 2020.

We see that in the earliest stages of the outbreak the CFR was much higher: 17.3% across China as a whole (in yellow) and greater than 20% in the centre of the outbreak, in Wuhan (in blue).

In the weeks that followed, the CFR declined. The WHO reports that “the standard of care has evolved over the course of the outbreak”. The CFR fell to 0.7% for patients with the onset of symptoms after February 1st.

We also see that the CFR was different in different locations. By 1st February the CFR in Wuhan was still 5.8% while it was 0.7% across the rest of China.

This makes clear that what we said about the CFR more generally is true for the CFR of COVID-19 specifically. The CFR is not only reflective of the disease itself, but specific to where and when people are diagnosed with the disease. It’s therefore incorrect to report it as a single point value for the disease, and instead requires us to also report the time and place.

Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 – Figure 4 in WHO (2020)47
Covid cfr in china over time

There are two reasons why the case fatality rate does not reflect the probability of death

If the case fatality rate does not tell us the probability that someone infected with the disease dies as a result, what does it tell us? And how does the CFR compare with the actual (unknown) probability?

One reason means that the probability that a person dies from COVID-19 once infected is lower than the CFR, and the other reason means the probability of death is higher. 

The probability that a particular person dies is lower than the reported case fatality rate when there are some cases that are not known. For COVID-19 this is currently the case.

As we have seen above, in our discussion on the difference between total and confirmed cases (here), we do not know the number of total cases. Not everyone is tested for COVID-19 and so the total number of cases is higher than the number of confirmed cases.

And whenever there are cases of the disease that are not counted, then the probability of dying from the disease is lower than the reported case fatality rate.

Or in one sentence. If it is the case that the number of total cases is higher than the number of confirmed cases, then the ratio between deaths and total cases is smaller than the ratio between deaths and confirmed cases.

This also means that when we compare the CFR between different countries, the differences do not only reflect higher rates of mortality, but also differences in the scale of testing efforts.

The probability that a particular person dies is higher than the reported case fatality rate when some people are currently sick and will die of the disease, but have not died yet. For COVID-19 this is currently the case.

Secondly, in interpreting the CFR during an outbreak we need to keep in mind that some people who are currently sick will eventually die from the disease. This means that they are currently counted as a case, but will eventually be counted as a death too.

This is true of the COVID-19 outbreak because the time from symptom onset to death is long – in the range of 2 to 8 weeks according to data from early cases (we discuss this here).48 

Some who are now counted as confirmed cases and who will die are of course currently not yet included in the current count of the number deaths. This means we would underestimate the eventual case fatality rate of the currently infected population.

Or in other words, the case fatality rate during an outbreak would be just as high as the eventual case fatality rate of this population if none of the current cases die. This would be wrong to assume.

Once an epidemic or outbreak is over, we can rely on aggregate statistics of cases and deaths to calculate the case fatality rate. But during an outbreak we need to be careful with how to interpret the CFR because the outcome (recovery or death) of a large number of cases is still unknown.

This is a common source for misinterpretation of a rising CFR in the earlier stages of an outbreak.49

This is what happened during the SARS-CoV outbreak in 2003: the CFR was initially reported to be 3-5% during the early stages of the outbreak, but had risen to around 10% by the end.50,51

While this was due to the described problem it had two bad consequences for the responses to the outbreak: The low numbers that were published initially resulted in an underestimate of the severity of the outbreak. And the rise of the CFR over time gave the wrong impression that SARS was becoming more deadly over time.

The current case fatality rate of COVID-19

Based on the discussion of the definition of the case fatality rate (CFR), we should stress again that there is no single figure of CFR for any particular disease. The CFR varies by location, and is typically changing over time.

However, with a good understanding of the measure and its limitations, CFR is helpful for understanding what we currently know about the severity of the disease and for responding accordingly.

In the period up to and including 15th March 2020, the global Case Fatality Rate for COVID-19 are as follows.

Case fatality rate globally = 3.7%
[based on 153,523 confirmed cases and 5736 deaths]

Case fatality rate in China: 3.9%
[based on 81,038 confirmed cases and 3204 deaths]

Case fatality rate for the rest of the world: 3.5%
[based on 72,475 confirmed and 2532 deaths]

As explained above, this number has changed and it will continue to change. It’s currently higher than the estimates of a CFR of around 2% that were published until early February.

As we’ve discussed above, this does not necessarily represent a worsening of the situation: as we saw during the SARS outbreak, the CFR can rise during an outbreak because the outcome of more cases becomes known. 

As we also explained above, it would be wrong to assume that this CFR would be true everywhere, because it is a global average of confirmed deaths and cases. The early CFR in Wuhan was very high as we see here; the large number of deaths there in the early period impacts the average.

Other studies for the Zhejiang province suggest that the CFR in China outside of Wuhan was likely lower.52

Case fatality rate of COVID-19 by age

Early data from China suggests that the elderly are most at risk

The total population-level estimate of the case fatality rate (CFR) above is useful for understanding the average severity of an outbreak, but does not tell us who within a population is most at risk. But this understanding is crucial in an outbreak. Understanding the relative risk to different sections of a population allows us to focus on the most vulnerable, and improve the allocation of health resources to those who need them most.

The Chinese Center for Disease Control and Prevention has published an analysis of recorded cases and deaths in China for the period until February 11th 2020 which provides a breakdown of all known cases, deaths and the CFR by specific demographics (age, sex, preexisting condition etc.).53

A breakdown of the CFR by age group is shown in the visualization. It shows very large differences of the CFR by age. 

For many infectious diseases young children are most at risk. We see this for malaria: the majority of deaths (57% globally) are in children under five years of age. The same was true for the largest pandemic in recorded history: During the ‘Spanish flu’ in 1918 it was primarily children and young adults who died from the pandemic (we write more about this in the article here).

For the COVID-19 cases in China the opposite seems to be true, at least based on the information available at the time of writing. The elderly are at the greatest risk of dying if infected with this virus.

Based on the data from China – shown in the visualization – 14.8% of those who are 80 years and older who were infected by COVID-19 died as a result. As explained above, these figures represent the share of people diagnosed as having the disease who die from it. This does not represent the share of people in the entire population who die from it.

The case fatality rate for children is much lower. There were no reported deaths in children under 10 years old; 0.2% of those aged 10 to 19 years who were diagnosed with COVID-19 died from it according to the early Chinese data.

As we show in the following section, the CFR for people with underlying health conditions is higher than for those without. One possible reason why the elderly might be most at risk is that they are also those who are most likely to have underlying health conditions such as cardiovascular diseases, respiratory diseases or diabetes. 

Coronavirus cfr by age in china 1

Case fatality rate of COVID-19 by preexisting health conditions

Early data from China suggests that those with underlying health conditions are at a higher risk

The visualization here shows the case fatality rate for populations within China based on their health status or underlying health condition.

This is based on the same data from the Center for Disease Control and Prevention’s initial breakdown of cases, deaths and CFR among specific demographics in the population.54 This analysis was based on recorded deaths and cases in China in the period up to February 11th 2020.

The researchers found that the CFR for those with an underlying health condition is much higher than for those without.

More than 10% of those diagnosed with COVID-19 who already had a cardiovascular disease, died as a result of the virus. Diabetes, chronic respiratory diseases, hypertension, and cancer were all risk factors as well, as we see in the chart.

The CFR was 0.9% for those without a preexisting health condition.

Above we saw that the elderly are most at risk of dying from COVID-19. This might be partly explained by the fact that they are also most likely to have underlying health conditions such as cardiovascular disease, respiratory disease and diabetes; these health conditions make it more difficult to recover from the COVID-19 infection.

Coronavirus cfr by health condition in china

Case fatality rate of COVID-19 compared to other diseases

How does the case fatality rate (CFR) of COVID-19 compare to other virus outbreaks and diseases?

Once again, we should stress what we discussed above. One has to understand the measurement challenges and the definitions to interpret estimates of the CFR for COVID-19, particularly those relating to an ongoing outbreak.

As comparisons, the table shows the case fatality rates for other disease outbreaks. The CFR of SARS-CoV and MERS-CoV were high: 10% and 34%, respectively.55

The US seasonal flu has a case fatality rate of approximately 0.1% – much lower than the current CFR for COVID-19.

Sources of data shown in the table:
SARS-CoV: Venkatesh, S. & Memish, Z.A. (‎2004)‎. SARS: the new challenge to international health and travel medicine. EMHJ – Eastern Mediterranean Health Journal, 10 (‎4-5)‎, 655-662, 2004.
SARS-CoV and MERS-CoV: Munster, V. J., Koopmans, M., van Doremalen, N., van Riel, D., & de Wit, E. (2020). A novel coronavirus emerging in China—key questions for impact assessment. New England Journal of Medicine, 382(8), 692-694.
Seasonal flu: US Centers for Disease Control and Prevention (CDC). Influenza Burden, 2018-19.
Ebola: Shultz, J. M., Espinel, Z., Espinola, M., & Rechkemmer, A. (2016). Distinguishing epidemiological features of the 2013–2016 West Africa Ebola virus disease outbreak. Disaster Health, 3(3), 78-88.
Ebola: World Health Organization (2020). Ebola virus disease: Factsheet.

Disease Estimated case fatality rate (CFR)
SARS-CoV 10%
Venkatesh and Memish (‎2004)‎
Munster et al. (2020)
MERS-CoV 34%
Munster et al. (2020)
Seasonal flu (US) 0.1%
US CDC
Ebola 50%
40% in the 2013-16 outbreak

WHO (2020)
Shultz et al. (2016)

How do case fatality rates from COVID-19 compare to those of the seasonal flu?

This question is answered in the visualization here. We compare the CFR during the outbreak of COVID-19 in China with the CFR of the US seasonal flu in 2018-19.

The case fatality rate of the seasonal flu in the US is around 0.1% to 0.2%, while the case fatality rate for COVID-19, measured in the cited study, was 2.3%.

The US data is sourced from the US CDC. Here we present an upper and lower estimate for the 2018-19 flu season. These two figures reflect whether we look at the percentage of deaths out of the number of symptomatic illnesses (giving us 0.1%), or the number of medical visits (giving us 0.2%). In the traditional calculation of CFR, we would tend to focus on the number of symptomatic illnesses. This is analogous to the number of confirmed cases, on which the COVID-19 figures are based. However, the US CDC derives these figures based on disease outbreak modelling which attempts to account for underreporting – you can read more about how it derives its annual flu figures here.

This means that some of the biases which tend to underestimate the actual number of cases have been corrected for. This is not the case for the COVID-19 figures, so it may be an unfair comparison.

Looking at estimates based on the number of medical visits may discount from the US seasonal flu data many of the kind of mild cases that may have been missed in the COVID-19 confirmed cases. However, this is likely to skew the comparison slightly in the other direction: we know that not all of the confirmed cases included in COVID-19 figures were of a severity such that they would have received a medical visit in the absence of the heightened surveillance of the outbreak.

So, here we present both figures of the US seasonal flu figures: the CFR based on symptomatic illnesses, and those based on medical visits (shown in square brackets). It’s likely that the fairest comparison to COVID-19 lies somewhere between these two values.

You can find the data for the reported cases, medical visits and deaths from the US Centers for Disease Control and Prevention (CDC) here. The CDC reports 35,520,883 symptomatic cases of influenza in the US and 34,157 deaths from the flu. To calculate the CFR based on symptomatic illnesses, we divide the number of deaths by the number of confirmed cases and find a case fatality rate of 0.1%.56

The CFRs for COVID-19 are again based on the numbers reported by the Chinese Center for Disease Control and Prevention.57 As before, the Chinese data refers to recorded deaths and confirmed cases in China as of February 11th 2020.

As calculated above, the global CFR for COVID-19 continues to change over time, and the global average CFR based on the WHO data is 3.7% (as of 15th March 2020).

While the CFR for COVID-19 is much higher than the CFR of the seasonal flu the two diseases are similar in the profile of the fatality rate by age: elderly populations have higher case fatality rates.

However, the CFR of COVID-19 is much higher for all age groups, including young people. On top of each bar we have indicated how much higher the CFR for COVID-19 is for each age group.

Covid 19 cfr by age vs. us seasonal flu 3

Data and dashboards from other sources

The World Health Organization (WHO), researchers from Johns Hopkins University, and other institutions all maintain datasets on the number of cases, deaths, and recoveries from the disease.

These are presented in a number of useful dashboards and websites listed below.

Johns Hopkins data on COVID-19

A dashboard is published and hosted by researchers at the Center for Systems Science and Engineering, Johns Hopkins University. It shows the number and location of confirmed COVID-19 cases, deaths, and recoveries in all affected countries.

The researchers have the intention to “continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak”.

Scientific Paper: The background paper for the Johns Hopkins’ dashboard was published by Dong, Du, and Gardner (2020) in The Lancet Infectious Disease.58 This paper also includes a comparison of this data with the data reported by the WHO and the Chinese CDC.

Data: All collected data in this effort from the Johns Hopkins University is made freely available by the researchers through this GitHub repository. You can download all the data shown in the dashboard. Information on the sources of their data can also be found directly there.

Link: Here is the Johns Hopkins dashboard. A here is a mobile friendly version of the same dashboard.

WHO data on COVID-19

The World Health Organization (WHO) publishes a dashboard similar to that of Johns Hopkins above.

The WHO dashboard on global cases and deaths is embedded here. In this dashboard it is possible to see up-to-date country specific data by selecting the country in the top right.

In addition to this dashboard, the WHO publishes daily Situation Reports which can be found here. It is the daily Situation reports that we rely on in our own published datasets on case and death numbers. Unlike the daily Situation Reports, the WHO dashboard is updated three times per day: any inconsistencies between the WHO dashboard and the data we present will be explained by this fact.

As we explained above, the Our World in Data team found several minor errors in the WHO data – we documented these errors, corrected them, reported them to the WHO, and are in close contact with colleagues at the WHO. Here is the documentation of our adjustments to the WHO data and an option to download all data.

nCoV-2019 Data Working Group data

The nCoV-2019 Data Working Group, which includes colleagues from the University of Oxford, publishes epidemiological data from the outbreak via this global dashboard. From this dashboard it is possible to obtain the underlying data which includes demographic and epidemiological descriptions of a long list of individual cases.

Their data on the list of cases includes individual travel history and key dates for each patient – date of onset of symptoms, date of hospitalisation and date of laboratory confirmation of whether the person was infected with the COVID-19 virus or not.

This data is intended to be helpful in the estimation of key statistics for the disease: Incubation period, basic reproduction number (R0), age-stratified risk, risk of importation.

In previous disease outbreaks such global individual data was not openly available.

Data from the Chinese Center for Disease Control and Prevention

The Chinese Center for Disease Control and Prevention publishes data via their dedicated site ‘Tracking the epidemic‘.