Coronavirus pandemic: daily updated research and data.
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Coronavirus – Tom VersionStatistics and Research

We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. Here you find the acknowledgements.

The data on the coronavirus pandemic is updated daily. Last update: May 27, 2020 (11:00, London time).

You can download our complete Our World in Data COVID-19 dataset.

Coronavirus Country Profiles

Which countries are doing better and which are doing worse? We built 207 country profiles which allow you to explore the statistics on the coronavirus pandemic for every country in the world.

Each profile includes interactive visualizations, explanations of the presented metrics, and the details on the sources of the data.

Every country profile is updated daily.

Every profile includes four sections:

  1. Deaths: How many people have died from the coronavirus? Is the number of deaths still increasing? How does the death rate compare to other countries?
  2. Testing: How much testing for coronavirus do countries conduct? When did they start and how does it compare with other countries?
  3. Cases: How many cases were confirmed? How many tests did a country do to find one COVID-19 case? And is your country bending the curve?
  4. Government responses: What measures did countries take in response to the pandemic?

Compare countries

This is our new Coronavirus Data Explorer. It brings together our global data on testing for COVID-19, and the counts of confirmed cases and deaths.

Each metric be seen in a straightforward line chart or in our trajectory charts, which align all countries at the start of the outbreak (here is how to read them). You can also switch to world maps for every metric, simply chose the Map tab below the chart.

What the data can and cannot tell us about the pandemic

Without data we can not understand the pandemic. Only based on good data can we know how the disease is spreading, what impact the pandemic has on the lives of people around the world, and whether the counter measures countries are taking are successful or not.

But even the best available data on the coronavirus pandemic is far from perfect.

In dedicated pages we present the latest data together with detailed explanations of what we can learn from this data: where does the data come from? What are the limitations that we need to be aware of? And what does the data tell us about the coronavirus pandemic?

The global data on the pandemic in detail:
If you want a global overview of the data start here:
All our work is free for everyone to use:
Risk factors

Our mission at Our World in Data is to provide the research and data on the world’s largest problems and how to make progress against them.

In recent work we have researched several of the risk factors for the coronavirus disease:


Build on top of our work freely

Country-by-country data on the pandemic

This page has a large number of charts on the pandemic. In the box below you can select any country you are interested in – or several, if you want to compare countries.

All charts on this page will then show data for the countries that you selected.

None selected

What is the total number of confirmed deaths?

Three points on confirmed death figures to keep in mind:

All three points are true for all currently available international data sources on COVID-19 deaths.

  • the actual total death toll from COVID-19 is likely to be higher than the number of confirmed deaths – this is due to limited testing and problems in the attribution of the cause of death; the difference between reported confirmed deaths and total deaths varies by country
  • how COVID-19 deaths are recorded may differ between countries (e.g. some countries may only count hospital deaths, whilst others have started to include deaths in homes)
  • the reported death figures on a given date does not necessarily show the number of new deaths on that day: this is due to delays in reporting.

→ We provide more detail on these three points in the section ‘Deaths from COVID-19: background‘.

Two tips on how you can interact with this chart

  • Add any other country to this chart: click on the Add country button to compare with any other country.
  • View this data on a world map: switch to a global map of confirmed deaths using the ‘MAP’ tab at the bottom of the chart.

Total confirmed deaths: how rapidly have they increased compared to other countries?

Charts which simply show the change in confirmed deaths over time are not very useful to answer the question of how the speed of the outbreak compares between different countries. This is because the outbreak of COVID-19 did not begin at the same time in all countries.

This chart here is designed to allow such comparisons.

The trajectory for each country begins on the day when that country had 5 confirmed deaths.

This allows you to compare how rapidly the number of confirmed deaths increased after the outbreak reached a similar stage in each country.

The grey lines in the background help you to see how rapidly the number of confirmed deaths is increasing

These lines show the trajectories for doubling times of 1, 2, 3, 5, and 10 days. If the slope that a country is on is steeper than a particular grey line, then the doubling time of confirmed cases in that country is faster than that. For example, there are several countries for which the slope was steeper than the ‘…every 2 days’ line – this means their death count doubled faster than every two days

How you can interact with this chart

  • Clicking on any country in the chart highlights that country. If you click on several countries you can create a view in which you can compare several countries.
  • Any country you might not see immediately you can find via the ‘Select Countries’ in the bottom left. Just type the name in the search box there.
  • To focus on the countries you highlighted click on ‘Zoom to selection’.

What is the daily number of confirmed deaths?

The previous charts looked at the increase of total confirmed deaths – this chart shows the number of confirmed deaths per day.

Why is it helpful to also look at the three-day rolling average of daily confirmed deaths?

For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day – but to the deaths reported on that day.

Since reporting can vary very significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to view the three-day rolling average of the daily figures. Above the chart you find the link to the rolling three-day average view.

→ We provide more detail in the section ‘Reported new cases on a particular day do not necessarily represent new cases on that day‘.

Another tip on how you can interact with this chart

By pulling the ends of the blue time slider you can focus the chart on a particular period. If you bring them together to one point in time then the line chart becomes a bar chart – this of course only makes sense if you compare countries (that is what the Add country button is for).

Daily confirmed deaths: are we bending the curve?

This trajectory chart shows whether countries make progress on bringing down the curve of new deaths.

To allow comparisons between countries the trajectory for each country begins on the day when that country first reported 5 daily deaths.

By default this chart is shown on a logarithmic vertical axis. We explain why in the next section. If you are not familiar with logarithmic axes we recommend you also look at this chart on a linear axis. The visual representation on these different axes can look very different.

This chart is shown on log axis, but you can switch to a linear axis

  • The default view on a logarithmic y-axis is helpful to compare the growth rates between countries: on a logarithmic axis the steepness of the line corresponds to the growth rate. [Here is an explanation for how to read logarithmic axes.]
  • But in this chart – as in many of our charts – you can switch to a linear axis – just click on ‘LOG’.

Global comparison: where are confirmed deaths increasing most rapidly?

Simply looking at the total or daily number of confirmed deaths does not allow us to understand or compare the speed at which the toll is rising.

The table here shows how long it has taken for the number of deaths to double in each country for which we have data. The table also shows how the total number of confirmed deaths, and the number of daily new confirmed deaths, and how those numbers have changed over the last 14 days.

A tip on how to interact with this table

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

World maps: Confirmed deaths relative to the size of the population

Why adjust for the size of the population?

It can be insightful to know not just how many have died compared to how many people actually live in that country.

For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the USA, with its population of 331 million.1 The death count in more populous countries tends to be higher – here you can see this correlation.

This is why the two maps below show the deaths per million people of each country’s population.

Three tips on how to interact with these maps

  • By clicking on any country on the map you see the change over time in this country.
  • By moving the time slider (below the map) you can see how the global situation has changed over time.
  • You can focus on a particular world region using the dropdown menu to the top-right of the map.

Why is data on testing important?

No country knows the total number of people infected with COVID-19. All we know is the infection status of those who have been tested. All those who have a lab-confirmed infection are counted as confirmed cases.

This means that the counts of confirmed cases depend on how much a country actually tests. Without testing there is no data.

Testing is our window onto the pandemic and how it is spreading. Without data on who is infected by the virus we have no way of understanding the pandemic. Without this data we can not know which countries are doing well, and which are just underreporting cases and deaths.

To interpret any data on confirmed cases we need to know how much testing for COVID-19 the country actually does.

The Our World in Data COVID-19 Testing dataset

Because testing is so very crucial to understanding the spread of the pandemic and responding appropriately we have focused our efforts on building a global dataset on COVID-19 testing. 

  • And as with all our work, it is freely accessible for everyone (the data is here on GitHub).

How many tests are performed each day?

This chart shows the number of daily tests per thousand people.

What is counted as a test?

The number of tests does not refer to the same in each country – one difference is that some countries report the number of people tested, while others report the number of tests (which can be higher if the same person is tested more than once). And other countries report their testing data in a way that leaves it unclear what the test count refers to exactly.

We indicate the differences in the chart and explain them in detail in our accompanying source description.

How many total tests have been performed?

This chart shows the number of total tests per thousand people.

In all our charts you can download the data

We want everyone to built on top of our work and therefore we always make all our data always available for download. Click on the ‘Data’-tab below the chart and you can download the shown data for all countries in a simple to use csv file.

How to interact with this chart

As before you can add and compare any selection of countries using the Add country button – once you have added more countries and you bring the ends of the blue time slider to the same point in chart you can create a bar chart.

Tests per case: how many tests to find one COVID-19 case?

This chart brings our data on testing together with the data on confirmed cases.

The chart answers the question: How many tests did a country do to find one COVID-19 case?

• Some countries – for example Taiwan and Vietnam – did a large number of tests per each confirmed case.

• For others the ratio is more than two orders of magnitude lower. These countries found a case for every few tests they did.

→ We discuss what this chart can – and can not – tell us about the data from a particular country in: What can data on testing tell us about the pandemic?

You can turn this bar chart into a line chart

Simply click somewhere into the timeline below the chart.

What is the total number of confirmed cases?

This chart shows the number of confirmed COVID-19 cases.

What is important to note about these case figures?
  • the reported case figures on a given date does not necessarily show the number of new cases on that day: this is due to delays in reporting;
  • the actual number of cases is likely to be much higher than the number of confirmed cases – this is due to limited testing.

→ We provide more detail on these points in the section ‘Cases of COVID-19: background‘.

Five quick reminders on how to interact with this chart

  • By clicking on Add country you can show and compare the data for any country in the world you are interested in.
  • If you click on the title of the chart, the chart will open in a new tab. You can then copy-paste the URL and share it.
  • You can switch the chart to a linear axis, by clicking on ‘LOG’.
  • If you move both ends of the time-slider to a single point you will see a bar chart for this point in time.
  • You can switch to the ‘MAP’ tab.

Total confirmed cases: how rapidly have they increased compared to other countries?

The trajectory for every country begins on the day when that country had 100 confirmed cases. This allows you to make comparisons of how quickly the number of confirmed cases has grown in different countries.

Keep in mind that in countries that do very little testing the total number of cases can be much higher than the number of confirmed cases shown here.

How you can interact with this chart

Clicking on any country in the chart highlights that country. If you click on several countries you can create a view in which you can compare several countries.

Any country you might not see immediately you can find via the ‘Select Countries’ in the bottom left. Just type the name in the search box there.

What is the daily number of confirmed cases?

The previous charts looked at the increase of total confirmed cases – this chart shows the number of confirmed cases per day.

Again you have the option to switch to the rolling three-day average via the link below the chart.

How you can interact with this chart

  • Add countries: add and compare any selection of countries using the Add country button.
  • Map view: switch to a global map of confirmed cases using the ‘MAP’ tab at the bottom of the chart.

Confirmed cases: How did the total and daily number change over time?

The previous charts allowed you to compare countries.

This is a chart that is helpful to understand the spread of the disease in a single country.

In yellow you see the number of daily new confirmed cases and in red the total sum of confirmed cases.

How you can interact with this chart

On these charts you see the button Change Country in the bottom left corner – with this option you can switch the chart to any other country in the world.

Daily confirmed cases: are we bending the curve?

To bring the pandemic to an end, every country has to bring the curve of daily cases down to zero.

This chart allows you to track whether countries are achieving this or not.

This chart shows the same data as before, but now adjusted for the size of the population – it shows daily confirmed cases per million people.

How you can interact with this chart

The default log view is helpful to compare the growth rates between countries: on a logarithmic scale the steepness of the line corresponds to the growth rate.

But in this chart, as in many of our charts, you can switch to a linear axis. Just click on ‘LOG’.

Here is an explanation for how to read logarithmic axes.

Global comparison: where are confirmed cases increasing most rapidly?

Simply looking at the total or daily number of confirmed cases does not allow us to understand or compare the speed at which this figure is rising.

The table here shows how long it has taken for the number of confirmed cases to double in each country for which we have data. The table also shows how the total number of confirmed cases, and the number of daily new confirmed cases, and how those numbers have changed over the last 14 days.

How you can interact with this table

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

World maps: Confirmed cases relative to the size of the population

For the same reason as before – differences in the population size between different countries are often large – it is insightful to compare the number of confirmed cases per million people.

Three tips on how to interact with these maps

  • By clicking on any country on the map you see the change over time in this country.
  • By moving the time slider (below the map) you can see how the global situation has changed over time.
  • You can focus on a particular world region using the dropdown menu to the top-right of the map.

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 look into this question in more detail here 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 (here).

How did confirmed deaths and cases change over time?

So far we focused first on confirmed deaths and then on confirmed cases.

This chart shows both metrics.

How you can interact with this chart

By now you know that in these charts it is always possible to switch to any other country in the world by choosing Change Country in the bottom left corner.You can sort the table by any of the columns by clicking on the column header.

The case fatality rate

The case fatality rate is simply the ratio of the two metrics shown in the chart above.

The case fatality rate is the number of confirmed deaths divided by the number of confirmed cases.

This chart here plots the CFR calculated in just that way. 

During an outbreak – and especially when the total number of cases is not known – one has to be very careful in interpreting the CFR. We wrote a detailed explainer on what can and can not be said based on current CFR figures.

Deaths and cases: our data source

Our World in Data relies on data from the European CDC

In this document and the many embedded and linked charts we report and visualize the data from the European Center for Disease Prevention and Control (ECDC).2 We make the data used in our charts and tables downloadable as a complete and structured .csv file here.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world. 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 European Centre for Disease Prevention and Control ECDC provides three statistical resources on the COVID-19 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 European CDC collects and aggregates data from countries around the world. The most up-to-date data for any particular country is therefore typically available earlier 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.

Deaths from COVID-19: background

What is counted as a death from COVID-19?

The attribution of deaths to specific causes can be challenging under any circumstances. Health problems are often connected, and multiplicative, meaning an underlying condition can often lead to complications which ultimately result in death.

This is also true in the case of COVID-19: the disease can lead to other health problems such as pneumonia and acute respiratory distress syndrome (ARDS).

So, how are deaths from COVID-19 recorded? What is and isn’t included in these totals?

As is standard in death reporting, countries are asked to follow the ‘cause of death’ classifications from the WHO’s International Classification of Diseases guidelines.3 However, countries also typically provide their own guidance to practitioners on how and when COVID-19 deaths should be recorded.

Let’s take a look at two concrete examples of national guidance: the United States and the UK. Both provide very similar guidelines for medical practitioners on the completion of death certificates. The US CDC’s Vital Statistics Reporting Guidance can be found here; the UK Government guidance is found here.4

Both guidelines state that if the practitioner suspects that COVID-19 played a role in an individual’s death it should be specified on the death certificate. In some cases, COVID-19 may be the underlying cause of death, having led to complications such as pneumonia or ARDS. Even when it’s the underlying and not the direct cause, COVID-19 should be listed.5

Although confirmed cases are reliant on a positive laboratory confirmation of the COVID-19 test, a laboratory diagnosis may not be required for it to be listed as the cause of death. In the UK guidelines, for example, it makes clear that practitioners should complete death certificates to the best of their knowledge, stating that “if before death the patient had symptoms typical of COVID19 infection, but the test result has not been received, it would be satisfactory to give ‘COVID-19’ as the cause of death, and then share the test result when it becomes available. In the circumstances of there being no swab, it is satisfactory to apply clinical judgement.”

This means a positive COVID-19 test result is not required for a death to be registered as COVID-19. In some circumstances, depending on national guidelines, medical practitioners can record COVID-19 deaths if they think the signs and symptoms point towards this as the underlying cause.

The US CDC guidelines also make this clear with an example: the death of an 86-year-old female with an unconfirmed case of COVID–19. It was reported that the woman had typical COVID-19 symptoms five days prior to suffering an ischemic stroke at home.Despite not being tested for COVID-19, the coroner determined that the likely underlying cause of death was COVID–19 given her symptoms and exposure to an infected individual.

Why are there delays in death reports?

Just as with confirmed cases, the number of deaths reported on a given day does not necessarily reflect the actual number of COVID-19 deaths on that day, or in the previous 24 hours. This is due to lags and delays in reporting.

Delays can occur for several reasons:

  • after a death certificate has been completed, inspection by post-mortem or laboratory testing may be required to verify the cause of death;
  • death certificates are then either automatically or manually coded: it is often the case that COVID-19 deaths are always manually coded (this is the case in the USA);
  • there can be significant delays in this coding process, particularly when there is a large increase in the number of deaths (this can be as long as 7 days in the US);
  • these figures are then collected in national registration statistics and reported to international sources.

This delay in reporting can be of the order of days – and sometimes as long as a week. This means the number of deaths reported on a given day are not reflective of the actual number of deaths which occurred on that day.

Total death figures are likely to be higher than confirmed deaths

What we know is the total number of confirmed deaths due to COVID-19 to date. Limited testing and challenges in the attribution of the cause of death means that the number of confirmed deaths may not be an accurate count of the true total number of deaths from COVID-19.

The European Center for Disease Prevention and Control (ECDC) – our data source on deaths – publishes daily updates of confirmed deaths due to COVID-19.

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-19.6 This means that some people who are currently infected with COVID-19 will die at a later date. 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 further below in this article 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).

Cases of COVID-19: background

How is a COVID-19 case defined?

In epidemiology, individuals which meet the case definition of a disease are often categorized on three different levels.

These definitions are often specific to the particular disease, but generally have some clear and overlapping criteria.

Cases of COVID-19 – as with other diseases – are broadly defined under a three-tier system: suspected, probable and confirmed cases.

  1. Suspected case
    A suspected case is someone who shows clinical signs and symptoms of having COVID-19, but has not been laboratory-tested.
  2. Probable case
    A suspected case with an epidemiological link to a confirmed case. This means someone who is showing symptoms of COVID-19 and has either been in close contact with a positive case, or, in a particularly COVID-affected area.7
  3. Confirmed case
    A confirmed case is “a person with laboratory confirmation of COVID-19 infection” as the World Health Organization (WHO) explains.8

Typically, for a case to be confirmed, a person must have a positive result from laboratory tests. This is true, regardless of whether they have shown symptoms of COVID-19 or not.

This means that the number of confirmed cases is lower than the number of probable cases, which is in turn lower than the number of suspected cases. The gap between these figures is partially explained by limited testing for the disease.

How are cases reported?

We have three levels of case definition: suspected, probable and confirmed cases. What is measured and reported by governments and international organizations?

International organizations – namely the WHO and European CDC – report case figures submitted by national governments. Wherever possible they aim to report confirmed cases, for two key reasons:

1. They have a higher degree of certainty because they have laboratory confirmation;

2. They held to provide standardised comparisons between countries.

However, international bodies can only provide figures as submitted by national governments and reporting institutions. Countries can define slightly different criteria for how cases are defined and reported.9 Some countries have, over the course of the outbreak, changed their reporting methodologies to also include probable cases.

One example of this is the United States. Until 14th April the US CDC provided daily reports on the number of confirmed cases. However, as of 14th April, it now provides a single figure of cases: the sum of confirmed and probable.

Suspected case figures are usually not reported. The European CDC notes that suspected cases should not be reported at the European level (although countries may record this information for national records) but are used to understand who should be tested for the disease.

Reported new cases on a particular day do not necessarily represent new cases on that day

The number of confirmed cases reported by any institution – including the WHO, the ECDC, Johns Hopkins and others – on a given day does not represent the actual number of new cases on that date. This is because of the long reporting chain that exists between a new case and its inclusion in national or international statistics.

The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:3

  1. Doctor or laboratory diagnoses a COVID-19 case based on testing or combination of symptoms and epidemiological probability (such as a close family member testing positive).
  2. Doctor or laboratory submits a report to the health department of the city or local district.
  3. Health department receives the report and records each individual case in the reporting system, including patient information.
  4. The ministry or another governmental organization brings this data together and publishes the latest figures.
  5. International data bodies such as the WHO or the ECDC can then collate statistics from hundreds of such national accounts.

This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases on that specific date.

The number of total cases is higher than the number of confirmed cases

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.

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).

The importance of testing

Testing is our window onto the pandemic and how it is spreading. Without testing we have no way of understanding the pandemic.

It is one of our most important tools in the fight to slow and reduce the spread and impact of the virus. Tests allow us to identify infected individuals, guiding the medical treatment that they receive. It enables the isolation of those infected and the tracing and quarantining of their contacts.10 And it can help allocate medical resources and staff more efficiently.11

In addition, testing for COVID-19 also informs our understanding of the pandemic and the risks it poses in different populations.

This knowledge is important if we are to properly assess the interventions that should be implemented, including very costly interventions such as social distancing and the shutdown of entire regions and industries.

Why data on testing is needed

Without data on COVID-19 we cannot possibly understand how the pandemic is progressing.

Without data we cannot respond appropriately to the threat; neither as individuals nor as a society. Nor can we learn where countermeasures against the pandemic are working. 

The number of confirmed cases is what informs us about the development of the pandemic.

But the confirmation of a case is based on a test. The World Health Organization defines a confirmed case as “a person with laboratory confirmation of COVID-19 infection”.12

Reliable data on testing is therefore necessary to assess the reliability of the data that informs us about the spread of the pandemic: the data on cases and deaths.

Different types of tests for COVID-19

There are many different technologies for COVID-19 testing, some currently available and some still in development. Trackers of the development, regulatory status and commercial release of different types of COVID-19 test are being compiled by Johns Hopkins University and the medical industry news website, 360Dx.

Broadly, we can divide these different tests into two kinds:

  • those that test for the presence of the virus, aiming to establish whether an individual is currently infected. The most common way of performing a test of the first type is with a ‘PCR’ test.13
  • those that test for the presence of antibodies, aiming to establish whether an individual has been infected at some point in the past.

Currently, we aim to include only these PCR tests in our dataset.

We do this for the following reasons:

  1. Our focus is on using testing data to help properly interpret the data we have on confirmed cases and deaths. Case confirmation is generally based on a positive result from a PCR test, in line with WHO recommendations.14 So including antibody tests in our figures would mean they were less useful for this purpose.
  2. Other kinds of test beyond PCR are not yet being widely used. This also means that data on how many of these tests have been conducted is very limited. Including such data in our counts when it is available would reduce the comparability of our data across countries.
  3. There are technical differences in how results from these different tests should be interpreted. Current data suggests that other existing testing technologies are subject to very different rates of false positive and false negative results than PCR tests.15 This is another reason why aggregating the data across these different types of test is not the best way of using testing data to help us understand the epidemic.

Some countries provide clear and helpful data on testing

Some countries present comprehensive, detailed and regularly updated data. Iceland (here) is one of these countries. Estonia (here) goes even further, showing breakdowns by age, gender and region.

For many countries however, available data on testing is either incomplete or else completely unavailable. This makes it impossible for their citizens and for researchers to assess the extent and significance of their testing efforts. 

Our current knowledge of COVID-19 testing – and more importantly of the pandemic itself – would be greatly improved if all countries were able to report all the testing data available to them in the way shown by the best examples. 

We need to understand what the published numbers on testing mean 

Those countries that do publish testing data often do not provide the required documentation to make it clear what the provided numbers precisely mean, and this is crucial for meaningful comparisons between countries and over time.

The key questions that any data description on testing data should answer are given in the following checklist. Clear answers to these questions are what is needed to properly interpret and compare published numbers.

For citizens to trust and understand the published data and for countries to learn from each other, it is crucial that every country provides the data on testing in a clearly documented way. We hope this checklist offers helpful guidance.

Our checklist for COVID-19 testing data

1) Is there no data – or it is just hard to find?

Many countries are not yet providing official figures. Others do not do so on a regular basis. The first question to ask, then, is if there is any testing data for a given country.

Equally important is to make the available data findable. Currently, the available data is often not easy to find, because some countries are releasing figures at unpredictable intervals in ad-hoc locations (including social media or press conferences).

2) What testing technologies are being used?

There are many different technologies for COVID-19 testing, some of which are already implemented, some currently available but not yet rolled out, and some still in development.  As we discuss here, these different tests are used with different objectives in mind, and there are technical differences in how results from these different testing technologies should be interpreted. 

It’s critical that governments provide a detailed and explicit account of the technologies that are being implemented as they get rolled out, disaggregating the test results accordingly. For citizens to trust and understand the published data, and for epidemiologists to incorporate the data into the models that inform public policy, it is crucial that every country provides the data on testing in a clearly documented way.

3) Do numbers refer to ‘performed tests’ or ‘individuals tested’?

The number of tests performed is different to the number of individuals tested. The reason for this is that it is common for COVID-19 testing that the same person is tested more than once.

Some countries report tests performed, while others report the number of individuals tested.

The source description should state clearly what is counted.

4) Are negative results included? Are pending results included?

It needs to be clear whether or not figures for the total number of tests performed, or the number of people tested, include negative test results, as well as the number of tests that are pending results.

Many sources report the number of individuals who are ‘suspected’ or have been ‘ruled out’. To be reliably included in test counts, it needs to be explicit whether such categories reflect the number of people who are awaiting test results or have tested negatively.

5) Do the figures include all tests conducted in the country, or only some? 

Figures reported by countries may only be partial if not all laboratories are reporting to the central authority.

The scope of testing data should be made explicit by the source. For instance, the US CDC make it clear that their figures do not include tests conducted in private labs.

6) Are all regions and laboratories within a country submitting data on the same basis?

Answers to the questions above may vary from region to region. In order to assess the reliability of aggregate testing data, it needs to be clear if heterogenous data is being summed together.

The US COVID Tracking Project, for instance makes it clear that their US totals combine data for tests performed and individuals tested, depending on which is reported by individual states.

7) What period do the published figures refer to?

Cumulative counts of the total number of tests should make clear the date from which the count begins. The key question that needs to be answered is whether the figures published at some date (attempt to) include all tests conducted up to that date.

Because the reporting of tests can take several days, for some countries figures for the last few days may not yet be complete. It needs to be made clear by the source if this may be the case. The US CDC, for instance, makes this clear.

8) Are there any issues that affect the comparability of the data over time?

If we want to look at how testing figures are changing over time, we need to know how any of the factors discussed above may have changed too.

The Netherlands, for instance, makes it clear that not all labs were included in national estimates from the start. As new labs get included, their past cumulative total gets added to the day they begin reporting, creating spikes in the time series.

9) What are the typical testing practices in the country?

Having a sense of how often and when individuals are tested, can help the users of these statistics understand how estimates of tests performed and individuals tested might relate to each other.

For instance, how many tests does a case investigation require? What are the eligibility criteria to be tested? Are health workers, or other specific groups, being routinely retested?

10) Might any of the information above be lost in translation?

People accessing data published in a language in which they are not fluent may misinterpret the data by mistranslating the provided text, which often includes technical terms.

Many countries report testing data in multiple languages – this helps disseminate the information to a broader audience, whilst helping prevent misinterpretations.

Our database on COVID-19 testing data

Our goal at Our World in Data is to provide testing data over time for many countries around the world.

We have started with this effort and will expand it in the coming days.

Our aim is to provide alongside the data a good understanding of the definitions used and any important limitations they might have. The checklist above is what guides our efforts.

At the end of this document you find descriptions of the data for each country. But in many cases sources do not yet provide the detailed descriptions of the data we would like. All the details we have been able to find so far are provided below.

We will be adding to the list of countries shown in the coming days.

The total number of tests performed or people tested so far

The two charts shown here show the total number of tests, or people tested, as indicated in the legend for each series.

Here we show the absolute number, and the number per thousand people of the country’s population – as line charts and map charts.

For comparisons across the series it is important to understand the definitions of the different measures. These are provided in the country by country notes below.

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on GitHub.

Tests per day

The two charts shown here show the daily number of tests, or people tested, in absolute terms and per thousand people respectively.

For comparisons across the series it is important to understand the definitions of the different measures. These are provided in the country by country notes below.

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on GitHub.


What can data on testing tell us about the pandemic?

Testing data provides us with two indicators of the quality of data on COVID-19

No country knows the true number of people infected with COVID-19. All we know is the infection status of those who have been tested.

The total number of people that have tested positive – the number of confirmed cases – is not the total number of people who have been infected. The true number of people infected with COVID-19 is much higher.

Whilst there is no way to infer the true number of infections from testing data, it can help give us a strong indication of the quality of a country’s data on the pandemic and an idea of how informative the number of confirmed cases in a country may be.

Testing coverage

The chart here shows a measure of testing coverage – tests per thousand people. 

Countries are reporting testing data in different ways: some report the number of tests, others report the number of people tested. This distinction is important – people may be tested many times, and the number of tests a person has is likely to vary across countries.16

Across different countries, we see an enormous range in testing coverage. In Iceland there have been more than 100 tests per thousand people – far more than in any other country. In Indonesia, testing coverage is very low – only 0.1 tests per thousand people.17

Generally, we would expect that more testing means more reliable data on confirmed cases, for two reasons.

Firstly, a greater degree of testing provides us with a larger ‘sample’ of people for which their infection status is known. If everybody was tested, we would know the true number of people who are infected.

Secondly, it may be the case that countries with a high capacity for testing do not need to ration tests as much. Where the capacity for testing is low, tests may be reserved (or ‘rationed’) for particularly high-risk groups. Such rationing is one of the reasons that tested people are not representative of the wider population.

As such, where testing coverage is higher, the ‘sample’ of tested people may provide a less biased idea of the true prevalence of the virus.18 

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on GitHub.

The number of tests per confirmed case

A further complication with using testing coverage as an indicator of reliability, is that the number of tests needed to have an accurate picture of the spread of the virus varies over the course of an outbreak.

At the beginning of an outbreak, where the number of people infected with the virus is low, a much smaller number of tests are needed to accurately assess the spread of the virus.

As the virus infects more people, testing coverage also needs to expand in order to provide a reliable picture of the true number of infected people.

For this reason it is helpful to look at the number of tests performed for each confirmed case. This gives us an indication of the scale of testing that accounts for the different stages each country may be in its outbreak.

The bar chart shows the number of tests, or people tested per confirmed case. The data can also be viewed over time in this chart.

The key insight from this metric is that there are very large differences between countries.

In some countries the number of tests are many times higher than the number of confirmed cases. As of 11 April, in Vietnam more than 400 tests had been conducted for each confirmed case. In Taiwan and Russia there had been around a hundred tests for each confirmed case.

But in other countries testing is very low relative to the number of confirmed cases. The US, the UK and Ecuador had performed around 5 tests or fewer for every confirmed case.

Download the data: we make our full testing dataset, alongside detailed source descriptions, available on GitHub.

What can we learn from these measures about the pandemic?

Both testing coverage and the number of tests per confirmed case help us understand what we can know about the true spread of the virus from data on confirmed cases.

But it is the number of tests per confirmed case that is arguably the most helpful in this regard, because this accounts for the fact that a smaller outbreak requires less testing.

Consider for instance the difference between three countries: the UK, Australia and Taiwan. 

These countries are highlighted in the chart here, which shows the number of tests per million against the number of confirmed cases per million. The dotted comparison lines show the points on the chart where the number of tests are a fixed number of times larger than the number of confirmed cases – 2, 5, 10, 20, 50, 100, 200 and 500 times larger.

In terms of testing coverage the UK appears to be ahead of Taiwan, with at least twice the number of people tested per thousand, as of 11 April.19

But on the same date, there were 60 times more confirmed cases per million in the UK than in Taiwan – 1,035 per million and 16 cases per million respectively. 

So whilst testing relative to population size is higher in the UK, testing relative to the size of the outbreak is much, much higher in Taiwan.

As of 11 April, in Taiwan one case was confirmed for every 120 tests. In the UK, a case was confirmed in fewer than every four tests.20

In Australia, testing coverage is much higher than in Taiwan. But in terms of the number of tests per confirmed case, the countries are much closer – one case was confirmed for every 55 tests in Australia as of 11 April. A number of issues with the data on testing – discussed here – mean that small differences between countries should not be overinterpreted.

But the very large differences – such as those seen between Taiwan, Australia and the UK – do tell us something important about the quality of the data.

A country that performs very few tests for each case it confirms is not testing widely enough for the number of confirmed cases to paint a reliable picture of the true spread of the virus. Whilst those people with the most severe symptoms may have been tested in such countries, there are likely to be many times more people with mild or no symptoms that were never tested.

Testing in the UK has not kept pace with the advancing outbreak. The number of people tested per confirmed case fell rapidly throughout March and early April – from more than 400, to less than 4. The current low level of testing, relative to the size of the outbreak, suggests that the true number of infections in the UK is likely to be far higher than the number of confirmed cases.

The large number of tests for each confirmed case in Taiwan and Australia suggests that the number of confirmed cases paint a much more reliable picture of the true number of infections in these countries.

Researchers from the London School of Hygiene and Tropical Medicine – Timothy Russel, Joel Hellewell, Sam Abbott and others – reach similar conclusions about the UK and Australia via a different method.21 They estimate the degree to which countries’ confirmed cases may underestimate total symptomatic cases by applying the case fatality rate (we explain this metric in detail here) observed in large studies in China and South Korea to data on the number of COVID-19 deaths in countries around the world.

They estimate that in Australia the number of confirmed cases reflect more than three quarters of the total number of symptomatic cases in the country. For the UK, they estimate that confirmed cases represent less than one in twenty symptomatic cases.22

As such, the gap between the UK and Australia in terms of the true number of infections is likely to be far higher than that indicated by their confirmed cases.

The intuition behind these researchers’ estimates is that where the number of confirmed cases looks low against the number of deaths, this is a clear indication that the true number of cases is likely to be much, much higher. But the fundamental reason for this is the limited extent of testing.

The rate of tests per case thus gives us another useful way of approaching the same question, by looking at the extent of testing relative to the number of cases directly.

Testing data: Source information country by country

Our detailed source information on all testing data can be found here.

The COVID-19 pandemic

In this section

The name of the disease and the virus

Diseases and the viruses or bacteria that cause them often have different names. For instance, the “human immunodeficiency virus”, HIV, causes “acquired immunodeficiency syndrome”, AIDS.

The virus causing the current outbreak is called severe acute respiratory syndrome coronavirus 2, shortened to SARS-CoV-2. The disease is called coronavirus disease, shortened to COVID-19.

These names have been assigned by the World Health Organization and the International Committee on Taxonomy of Viruses.23

The WHO also refers 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?

The outbreak was first noticed in the city of Wuhan, China. Wuhan is the capital of the Hubei Province and has about 11 million inhabitants. On 29 December 2019, Chinese authorities identified a cluster of similar cases of pneumonia in the city.

These cases were soon determined to be caused by a novel coronavirus that was later named SARS-CoV-2.24

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. By March it developed into a global pandemic and was declared as such by the WHO.

What is a coronavirus?

Although people often refer to the virus causing COVID-19 as “the coronavirus”, there are many different coronaviruses.

The term refers to a group of viruses that are common in humans: coronaviruses are the cause for around 30% of cases of the common cold.25 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 deaths26 and MERS in 2012 that resulted in 862 deaths.27

Why do we focus on growth rates during the pandemic?

In an outbreak of an infectious disease it is important to not only study the number of cases and deaths, but also the growth rate at which these numbers are increasing.

This is because even if the current numbers 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 three days ago there had been 500 confirmed deaths in total, and today we have reached 1,000, then the doubling time is three days.28

The doubling time of deaths has changed and it will change in the future; we should not naively extrapolate the current doubling time to conclude how many people will die.29

If deaths go up by a fixed number over a fixed period – say, by 500 every two days – then we call that “linear” growth. But if they keep on doubling within a fixed time period – say, every three days – then we call that “exponential” growth.

Humans tend to think in terms of linear growth, and research shows that we find exponential growth hard to understand. Below, we’ll try to explain it in a way that makes it more intuitive.

Understanding exponential growth

To get a sense of the difference between linear and exponential growth, take a look at the visualization here. The numbers shown do not represent any actual data points: they’re simply an illustration.

Both lines start at value of 10 at time 0. The linear trend (in blue) increases by 10 at every time increment (10, 20, 30, 40, 50, 60).

The exponential growth line (in red) doubles each increment (10, 20, 40, 80, 160, 320).

Early on in the growth chart the absolute difference remains small, but over time the exponential growth leads to very large numbers – to see this pull the blue time slider below the chart slowly to the right.

If, during an outbreak, the time it takes for deaths to double remains constant, then the disease is spreading exponentially.

Under exponential growth 500 deaths grow to more than 1 million deaths after 11 doubling times.30 And after 10 more doubling times it would be 1 billion deaths.

This isn’t to say that we should expect to see numbers like that in the COVID-19 outbreak, but it is a reminder that exponential growth leads to very large numbers very quickly, even when starting from a low base.

Psychologists find that humans tend to think in linear growth processes (1, 2, 3, 4) even when it doesn’t appropriately describe the reality in front of our eyes. This is called exponential growth bias.31

There is a straightforward question that most people would like answered. If someone is infected with COVID-19, how likely is that person to die? 

This question is simple, but surprisingly hard to answer.

Here we explain why that is. We’ll discuss the “case fatality rate”, the “crude mortality rate”, and the “infection fatality rate”, and why they’re all different.

The key point is that the “case fatality rate”, the most commonly discussed measure of the risk of dying, is not the answer to the question, for two reasons. One, it relies on the number of confirmed cases, and many cases are not confirmed; and two, it relies on the total number of deaths, and with COVID-19, some people who are sick and will die soon have not yet died. These two facts mean that it is extremely difficult to make accurate estimates of the true risk of death.

The case fatality rate (CFR)

In the media, it is often the “case fatality rate” that is talked about when the risk of death from COVID-19 is discussed.32 This measure is sometimes called case fatality risk or case fatality ratio, or CFR.

But this is not the same as the risk of death for an infected person – even though, unfortunately, journalists often suggest that it is. It is relevant and important, but far from the whole story.

The CFR is very easy to calculate. You take the number of people who have died, and you divide it by the total number of people diagnosed with the disease. So if 10 people have died, and 100 people have been diagnosed with the disease, the CFR is [10 / 100], or 10%.

But it’s important to note that it is the ratio between the number of confirmed deaths from the disease and the number of confirmed cases, not total cases. That means that it is not the same as – and, in fast-moving situations like COVID-19, probably not even very close to – the true risk for an infected person.

Another important metric, which should not be confused with the CFR, is the crude mortality rate.

The crude mortality rate

The “crude mortality rate” is another very simple measure, which like the CFR gives something that might sound like the answer to the question that we asked earlier: if someone is infected, how likely are they to die?

But, just as with CFR, it is actually very different.

The crude mortality rate – sometimes called the crude death rate – measures the probability that any individual in the population will die from the disease; not just those who are infected, or are confirmed as being infected. It’s calculated by dividing the number of deaths from the disease by the total population. For instance, if there were 10 deaths in a population of 1,000, the crude mortality rate would be [10 / 1,000], or 1%, even if only 100 people had been diagnosed with the disease.

This difference is important: unfortunately, people sometimes confuse case fatality rates with crude death rates. A common example is the Spanish flu pandemic in 1918. One estimate, by Johnson and Mueller (2002), is that that pandemic killed 50 million people.33 That would have been 2.7% of the world population at the time. This means the crude mortality rate was 2.7%.

But 2.7% is often misreported as the case fatality rate – which is wrong, because not everyone in the world was infected with Spanish flu. If the crude mortality rate really was 2.7%, then the case fatality rate was much higher – it would be the percentage of people who died after being diagnosed with the disease. [We look at the global death count of this pandemic and others here.]

What we want to know isn’t the case fatality rate: it’s the infection fatality rate

Before we look at what the CFR does tell us about the mortality risk, it is helpful to see what it doesn’t.

Remember the question we asked at the beginning: if someone is infected with COVID-19, how likely is it that they will die? The answer to that question is captured by the infection fatality rate, or IFR.

The IFR is the number of deaths from a disease divided by the total number of cases. If 10 people die of the disease, and 500 actually have it, then the IFR is [10 / 500], or 2%.34,35,36,37,38

To work out the IFR, we need two numbers: the total number of cases and the total number of deaths. 

However, as we explain here, the total number of cases of COVID-19 is not known. That’s partly because not everyone with COVID-19 is tested.39,40 

We may be able to estimate the total number of cases and use it to calculate the IFR – and researchers do this. But the total number of cases is not known, so the IFR cannot be accurately calculated. And, despite what some media reports imply, the CFR is not the same as – or, probably, even similar to – the IFR. Next, we’ll discuss why.

Interpreting the case fatality rate

In order to understand what the case fatality rate can and cannot tell us about a disease outbreak such as COVID-19, it’s important to understand why it is difficult to measure and interpret the numbers.

The case fatality rate isn’t constant: it changes with the context

Sometimes journalists talk about the CFR as if it’s a single, steady number, an unchanging fact about the disease. This is a particular bad example from the New York Times in the early days of the COVID-19 outbreak.

But it’s not a biological constant; instead, it reflects 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 doesn’t just depend on the disease itself, but also on the treatment they receive, and on the patient’s own ability to recover from it.

This means that the CFR can decrease or increase over time, as responses change; and that it can vary by location and by the characteristics of the infected population, such as age, or sex. For instance, older populations would expect to see a higher CFR from COVID-19 than younger ones.

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

The case fatality rate of COVID-19 is not constant. You can see that in the chart below, first published in the Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), in February 2020.41

It shows the CFR values for COVID-19 in several locations in China during the early stages of the outbreak, from the beginning of January to 20th February 2020.

You can 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).

But in the weeks that followed, the CFR declined, reaching as low as 0.7% for patients who first showed symptoms after February 1st. The WHO says that that is because “the standard of care has evolved over the course of the outbreak”.

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

This shows that what we said about the CFR generally – that it changes from time to time and place to place – is true for the CFR of COVID-19 specifically. When we talk about the CFR of a disease, we need to talk about it in a specific time and place – the CFR in Wuhan on 23rd February, or in Italy on 4th March – rather than as a single unchanging value.

Case fatality ratio for COVID-19 in China over time and by location, as of 20 February 2020 – Figure 4 in WHO (2020)42
Covid cfr in china over time

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

If the case fatality rate does not tell us the risk of death for someone infected with the disease, what does it tell us? And how does the CFR compare with the actual (unknown) probability?

There are two reasons why we would expect the CFR not to represent the real risk. One of them would tend to make the CFR an overestimate – the other would tend to make it an underestimate.

When there are people who have the disease but are not diagnosed, the CFR will overestimate the true risk of death. With COVID-19, we think there are many undiagnosed people.

As we saw 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, 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. Remember our imaginary scenario with 10 deaths and 100 cases. The CFR in that example is 10% – but if there are 500 real cases, then the real risk (the IFR) is just 2%.

Or in one sentence. If 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 of course assumes that there is not also significant undercounting in the number of deaths; it’s plausible that some deaths are missed or go unreported, but we’d expect the magnitude of undercounting to be less than for cases.

Importantly, this means that the number of tests carried out affects the CFR – you can only confirm a case by testing a patient. So when we compare the CFR between different countries, the differences do not only reflect rates of mortality, but also differences in the scale of testing efforts.

When some people are currently sick and will die of the disease, but have not died yet, the CFR will underestimate the true risk of death. With COVID-19, many of those who are currently sick and will die have not yet died. Or, they may die from the disease but be listed as having died from something else.

In ongoing outbreaks, 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 means the CFR right now is an underestimate of what it will be when the disease has run its course.

With the COVID-19 outbreak, it can take between two to eight weeks for people to go from first symptoms to death, according to data from early cases (we discuss this here).43 

This is not a problem once an outbreak has finished. Afterwards, the total number of deaths will be known, and we can use it to calculate the CFR. 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.44

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.45,46

This is not just a problem for statisticians: it had real negative consequences for our understanding of 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. These errors made it harder to come up with the right response.

The current case fatality rate of COVID-19

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.

As this paper shows47, CFRs vary widely between countries, from 0.2% in Germany to 7.7% in Italy. But it says that this is not necessarily an accurate comparison of the true likelihood that someone with COVID-19 will die of it.

We do not know how many cases are asymptomatic versus symptomatic, or whether the same criteria for testing are being applied between countries. Without better and more standardised criteria for testing and for the recording of deaths, the real mortality rate is unknown. As the paper says, to understand the differences in CFR and how they should guide decision-making, we need better data.

But if we’re careful to acknowledge its limitations, CFR can help us to better understand the severity of the disease and what we should do about it.

This chart shows how these early CFR values compare. You can see the total number of confirmed cases of COVID-19 (on the x-axis, going across) versus the total number of deaths (on the y-axis, going up).

The grey lines show a range of CFR values – from 0.25% to 10%.

Where each country lies indicates its CFR – for instance, if a country lies along the 2% line, its current confirmed cases and death figures indicate it has a CFR of 2%.

The second chart shows how the CFR has changed over time in countries that have had over 100 confirmed cases.

We have excluded countries which still have a relatively small number of confirmed cases, because CFR is a particularly poor metric to understand mortality risk with a small sample size.

We see this if we look at the trajectory of cases and deaths in Iran: on February 24th it had 2 confirmed cases and 2 deaths, an implausible CFR of 100%. With time its CFR begins to fall, as the number of confirmed cases increases. By the time it has seen hundreds of cases, the CFR drops to around the level seen in other countries.

Case fatality rate of COVID-19 by age

Current data across countries suggests that the elderly are most at risk

It’s helpful to estimate the risk of death across a population – the average IFR, the chance of death if a random person in the country were to catch the disease, which we discussed above. It helps us know the severity of an outbreak.

But during an outbreak, it’s also crucial to know which groups within a population are most at risk. If we know which sections of society are most likely to die, or suffer other serious consequences, then that allows us to direct our resources towards the most vulnerable, who need them the most.

In the chart below, we see a breakdown of the CFR by age group across various countries who have made demographic data on confirmed cases and deaths available. It shows very large differences of the CFR by age. 

This data is based on the number of confirmed cases and deaths in each age group as reported by national agencies. The figures come from the Chinese Center for Disease Control and Prevention (CDC) as of 17th February; Spanish Ministry of Health as of 24th March; Korea Centers for Disease Control and Prevention (KCDC) as of 24th March; and the Italian National Institute of Health, as presented in the paper by Onder et al. (2020) as of 17th March.48,49

Again it’s important to stress that the CFR simply represents the number of deaths divided by the number of confirmed cases. It does not tell us the true risk of death, which (as we say above) is much harder to estimate. The CFR changes over time, and differences between countries do not necessarily reflect real differences in the risk of dying from COVID-19. Instead, they may reflect differences in the extent of testing, or the stage a country is in its trajectory through the outbreak.

For many infectious diseases young children are most at risk. For instance, in the case of malaria, the majority of deaths (57% globally) are in children under five. The same was true for the largest pandemic in recorded history: During the ‘Spanish flu’ in 1918, children and young adults were at the greatest risk from the pandemic (we write more about this in the article here).

For COVID-19 cases the opposite seems to be true. The elderly are at the greatest risk of dying, if infected with this virus.

It may not simply be that the older you get, the more at risk you are, though. As we show in the next section, the CFR for people with underlying health conditions – such as cardiovascular diseases, respiratory diseases or diabetes – is higher than for those without. Elderly people are more likely to have those conditions, which is likely to be part of the reason why the elderly are most at risk from COVID-19. 

Case fatality rate of COVID-19 by age group across countries.50,51
Covid cfr by age

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 chart 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 as we discussed in the section on age.52 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. For instance, more than 10% of people with a cardiovascular disease, and who were diagnosed with COVID-19, died. Diabetes, chronic respiratory diseases, hypertension, and cancer were all risk factors as well, as we see in the chart.

By comparison, the CFR was 0.9% – more than ten times lower – for those without a preexisting health condition.

Above we saw that the elderly are most at risk of dying from COVID-19. As we said there, that 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.53

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.

DiseaseEstimated case fatality rate (CFR)
SARS-CoV10%
Venkatesh and Memish (‎2004)‎
Munster et al. (2020)
MERS-CoV34%
Munster et al. (2020)
Seasonal flu (US)0.1-0.2%
US CDC
Ebola50%
40% in the 2013-16 outbreak

WHO (2020)
Shultz et al. (2016)

Healthcare capacity

The capacity of the healthcare system is of great importance in any country’s response to the pandemic. If there are more critically ill people than there are intensive care facilities, people will die who otherwise might not have.

The two maps here show the number of medical doctors and hospital beds relative to the size of each country’s population.

Countries with more elderly people may be hardest hit

As we saw above, the current evidence suggests that older people are at a higher risk from COVID-19 than younger ones. That means that countries with more older people can expect to suffer worse consequences than those with more younger ones, all else being equal.

The map shows the share of the population that is 70 years and older.

In our entry on the age structure we study this in much more detail.

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.

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.54 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. And 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.

Here is the WHO dashboard on global cases and deaths. In this dashboard it is possible to see up-to-date country specific data.

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.

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‘.

The mission of Our World in Data is to make the best research on the world’s largest problems available and understandable. While most of our work focuses on large problems that humanity has faced for a long time – such as child mortalitynatural disasterspoverty 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].

  • Pneumonia – Severe cases of COVID-19 can progress to pneumonia.55 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.
  • Vaccination – Vaccines are key in making progress against infectious diseases and save millions of lives every year. A vaccine against COVID-19 would be the scientific breakthrough that could end this pandemic.
  • Healthcare financing – A strong healthcare system is key to make progress against the infectious disease. In this entry we are studying how healthcare is financed.
  • Causes of death – 56 million people die every year. What do they die from? How did the causes of death change over time?
  • Smoking – In this entry we study the prevalence of smoking and its global health impact. Smoking is a risk factor for COVID-19 as it increases the chance of getting infected and can increase the severity of the disease.56

Acknowledgements

We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. Tom Chivers we would like to thank for his editorial review and feedback.

And we would like to thank the many hundreds of readers who give us feedback on this work every day. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account. Thank you all.