COVID19

About this dashboard

Johns Hopkins University maintains a database of world-wide confirmed COVID-19 cases (GitHub, Dashboard). This dashboard lets you explore the data further by visualising data regarding the total confirmed cases, confirmed deaths and confirmed recoveries. These numbers can be seen nominally as well as relative to the country's population. You can compare these data across countries and you can fit exponential growth models to individual countries to see how the disease has spread so far.

Authors: This dashboard was created by Nikolaj Theodor Thams, Martin Emil Jakobsen, Phillip Bredahl Mogensen, all PhD-students in Statistics at the University of Copenhagen. We would be happy to receive your feedback.

Source code: The source code for this app is freely available on GitHub.

COVID19 data: Johns Hopkins University (GitHub, Dashboard).

SSI data: Danish State Serum Institute (reports).

Update frequency: The Johns Hopkins github database is currently updated once daily, shortly after which we feed this dashboards with the updated data. The SSI reports are published daily between 12.00 and 13.00 Central European Time.

Validity of data: The situation on COVID-19 is developing rapidly, and we're vastly grateful for the work done by Johns Hopkins. While there may be misreportings in the data, they are often quickly solved.

Population data: https://www.worldometers.info/world-population/population-by-country/

Terms of use: Please be aware of the terms of use for the Johns Hopkins data. If you wish to use our dashboard, naturally you must obey to these. Further, we take no responsibilities for the correctness of displayed data, processing nor modelling.

The Danish State Serum Institute (SSI) are at the time of writing (March 20, 2020) publishing daily reports on the development of COVID-19 in Denmark. The full reports can be found here. Included in these reports are, amongst other things, daily data on the number of tested people in Denmark as well as the number of confirmed positive tests. Furthermore, SSI provides cumulative data on the age distribution among the tested people. On this page you can visualise these data.

Usage note 1: SSI only reports the daily number of test for the last 14 days and aggregate everything before that. We scrape data daily, which means you can here view data from every day since the \(14^\text{th}\) of March, 2020.

Usage note 2: When viewing cumulative data, be aware that the Danish testing protocol was changed on the \(12^\text{th}\) of March. After this date, only patients with severe symptoms are tested. In comparison, before this date, there was a large effort made to test everyone returning from trips to primarily Austria and Italy. Generally, these people were often infected, leading to an unnaturally high positive test rate.

View:

Daily number of tests and percentage positive

Cumulative Number of tests and percentage positive by age group

Y-axis scale

Original scale

Logarithmic scale

Compare estimator to

Deaths

Confirmed cases

Countries

Days from illness onset to death

Case fatality rate numbers from South Korea are used in the estimation.

Solid curves indicate confirmed numbers. Shaded regions are estimated number of infected, measured from illness onset.

Contributors:
Rune Christiansen: Ph.D. student in statistics, University of Copenhagen.
Phillip Mogensen: Ph.D. student in statistics, University of Copenhagen.
Jonas Peters: Professor of statistics, University of Copenhagen.
Niklas Pfister: Assistant professor of statistics, University of Copenhagen.
Nikolaj Thams: : Ph.D. student in statistics, University of Copenhagen.

About this analysis: In this analysis, we try to estimate the cumulative number of actual infected individuals above 30, as opposed to reported number of infected, which may be orders of magnitude lower.

While there are substantial shadow figures for infected, the death figures are more reliable. The number of deaths today gives an indication of the number of infected individuals 2-3 weeks in the past. Fundamentally, we model the number of infected individuals by using data about the South Korean death rate, which is believed to be more accurate than many other, due to the large amounts of tests performed in South Korea. We stress that we only estimate infected above 30, because since very few people below 30 die, it is impossible to estimate the infected from the deaths alone.

Our approach incorporate varying demographics across countries and varying mortality across age groups. Technical details of our approach can be found in this white paper (which is still work in progress).

Assumptions: To get these estimates, we assume that the true death rates in each age group is the same for all countries. Of course, this is not the case; across countries, people have different genetic compositions, there are different practices regarding treatment, differences in health-care quality and so forth. In other words, we know that this assumption is violated. However, because we do not have any data from a randomized study, it is next to impossible to good estimates of the death rates for every single country. Because South Korea has tested extensively, it is likely that the death rates we see there are the closest to what we could hope to obtain from a randomized study. For deaths rates that are actually higher than those observed in South Korea, the estimated numbers above will be too high and vice versa.

Hackathon This methodology was developed as part of the #WirVsVirus hackathon (March 20th-22nd, 2020). See also the devpost page related to our solution and this 2 minute pitch describing our solution (both in German).

Data source: Johns Hopkins University (infection data) and UN data (demographic data).