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Can be brought by customers Please consult with us on weight and other matters. Without data on who is infected by the virus we have no way of understanding the pandemic. Without this data we cannot 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 the country actually does.
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 testing. To be able to properly monitor the spread of the virus, countries with more widespread outbreaks need to do more testing. So one important way to understand if countries are testing sufficiently is to ask: What share of the tests confirm a case?
What is the positive rate? This is what the map here shows. If you are interested in the development over time you can simply click on the country in the map or switch to the Chart-tab at the bottom of the visualization.
Countries that do very few tests per confirmed case are unlikely to be testing widely enough to find all cases. In countries that test very little in relation to their outbreak — shown in shades of red in the chart — many cases are likely to go unreported. In these countries, the number of confirmed cases indicated may represent only a fraction of the total number of cases. How many tests have countries done for each confirmed case in total across the outbreak?
How does these numbers look when we compare daily tests and daily new confirmed cases in absolute terms? This scatter chart provides another way of seeing the extent of testing relative to the scale of the outbreak in different countries.
The chart shows the daily number of tests vertical axis against the daily number of new confirmed cases horizontal axis , both per million people. Looking downward on the chart, we see some countries doing ten or a hundred times fewer tests than other countries with a similar number of new confirmed cases. Conversely, looking to the right, we see some countries find ten or a hundred times more cases than others out a similar number of tests.
Where the number of confirmed cases is high relative to the extent of testing, this suggests that there may not be enough tests being carried out to properly monitor the outbreak. In such countries, the true number of infections may be far higher than the number of confirmed cases. In a separate post we discuss how epidemiological models of COVID help us estimate the true number of infections. This chart shows the number of daily tests per thousand people. Because the number of tests is often volatile from day to day, we show the figures as a seven-day rolling average.
The number of tests does not refer to the same thing 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 descriptions. To understand the risks and respond appropriately we would also want to know the mortality risk of COVID — the likelihood that someone who is infected with the disease will die from it.
We look into this question in more detail on our page about the mortality risk of COVID , where we 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. How do the cumulative number of confirmed deaths and cases compare? The case fatality rate is the number of confirmed deaths divided by the number of confirmed cases. 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.
This tracker collects publicly available information on 17 indicators of government responses, spanning containment and closure policies such as school closures and restrictions in movement ; economic policies; and health system policies such as testing regimes. Travel bans, stay-at-home restrictions, school closures — how have countries responded to the pandemic? Explore the data on all policy measures. The chart here shows how governmental response has changed over time.
It shows the Government Stringency Index — a composite measure of the strictness of policy responses. The index on any given day is calculated as the mean score of nine policy measures, each taking a value between 0 and You can see all of these separately on our page on policy re s ponses. There you can also compare these responses in countries across the world.
None selected. Should you have further questions on the data, do not hesitate to contact us. Estimates for national accounts such as national income and national wealth found on WID. First, we release detailed series for national wealth accounts, which usually cannot be found on other portals. Next, we include corrections for offshore wealth and offshore capital income, so that our series on foreign capital income inflows and outflows are consistent at the global level e.
Finally, reliable series for the consumption of fixed capital capital depreciation estimates are not readily available for a large number of countries, so we combine various sources and develop new methods to derive consistent global series. As a consequence, we are able to offer consistent global series on national income — i.
GDP minus consumption of fixed capital, plus net foreign income — which do not exist elsewhere. It should also be noted that default monetary values for Eurozone countries are displayed in PPP Euros and are thus different from Market exchange rate Euros.
Market exchange rates values can be obtained in our custom menus. All these methodological choices can explain slightly different values between WID. They are described in the Metadata associated to each variable and in the associated methodological documents. In addition, it should be noted that there are specific countries such as China where there is substantial controversy about price deflators and aggregate real growth.
In such cases we review all existing series and attempt to combine them in the most sensible manner. This is fully explained in the country-specific papers. There are already many on line economic data portals, why using WID. Over the past decades, the increase in economic inequalities was largely driven by a rise in income and wealth accruing to the top of the distribution.
However, household surveys, the data sources traditionally used to observe these dynamics, do not capture these evolution very well. Users should feel free to contact us if they notice any inaccuracies. Note In spite of the utmost care taken in constructing these databases, we cannot be certain that all mistakes, including incorrect or omitted words, have been eliminated. We do not assume any responsibility for trouble caused through the use of these databases.
Nevertheless, in order to improve the quality of the data provided, we would be very grateful for any information regarding inadequacies that users may notice.
We regret that we cannot answer substantive reference inquiries. Thanks I wish to express my thanks to the many people who contributed to this project. Without their efforts the compiling of these databases would not have been possible. Last Updated:
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