When epidemiologists examine COVID-19 data to detect concerning trends, they often reference metrics such as:
- The epidemic growth rate (how fast the daily case count is rising)
- The effective reproductive number (the average number of secondary cases per known case)
- The percent of tests that are positive
- Measures of population mobility
- The magnitude and trends of daily case and death counts, including in relation to previous peaks
- Unusual departures from the expected trend, as detected by mathematical models
- The context of COVID-19 trends in the surrounding region
World Health Organization (WHO) has now expanded its publicly available COVID-19 Explorer dashboard to include these metrics and highlight concerning developments.
To access these, you can click on “Explore > Explore the Data” on the top right of the WHO COVID-19 dashboard (covid19.who.int) or simply click here. From there, you need to click on the “Trend analysis” tab.
Below we talk you through some of these new tools.
Are there any red flags?
In the Flags sub-tab, you can visualize how a country, area or territory is doing on these metrics with coloured flags: a blue flag means that the numbers are pointing in the right direction, while a red flag means the opposite.
Using machine learning to detect departure from trends
We have used mathematical models to produce a range of expected values using the previous six weeks of data, accounting for patterns in testing and weekday effects among other factors.
To see these, click on the Departures from trend sub-tab. The charts show both expected trends and actual values for daily reported cases. You can see if the cases are following recent trends or deviating from these. Red dots mean that there has been an above-expected increase in cases, indicating an acceleration of case numbers.
Providing context
To contextualize the currently reported cases, it helps to see where a country is in comparison with the last peak. It’s also useful to see how fast the situation is changing.
On the vertical axis of the Dynamics pin plot sub-tab, you can see the recent daily case count as a proportion of the previous peak case count experienced by that country, area or territory. On the horizontal axis, you can see how fast the epidemic is growing each day. A growth rate can either be negative (the epidemic is declining), at 0 (the epidemic is stable), or positive (the epidemic is growing).
Together, this means that points in the top-right quadrant are the most worrying: they are places seeing the fastest, most unprecedented growth.
Finally, the colour of each point represents the number of recent days that the trend exceeded what was expected. The more red a point is, the longer has been the “acceleration” beyond what was expected.
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General Disclaimer
These tools are intended for use by policymakers, COVID-19 responders, and the interested public who want to track epidemiological trends by country/area/territory. Use of these tools should not replace trained interpretation of public health data. All data used are publicly available and are described in the “About” tab of the dashboard. We encourage you to supplement these tools with use of other tabs in the dashboard, such as the “Regional overview” and “Country/Area/Territory” to view other detailed information.
This dashboard contains works in progress. It implements an automated data pipeline for country/area/territory-level surveillance of COVID-19 dynamics using publicly available data. Its content should not be used for publications without explicit agreement from the authors. The accuracy of the estimates provided in these analyses is contingent on data quality and availability. Results presented here do not represent the official view of the WHO, its staff or consultants.
Caution must be taken when interpreting all data presented, and differences between information products published by WHO, national public health authorities, and other sources using different inclusion criteria and different data cut-off times are to be expected. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. All counts are subject to variations in case detection, definitions, laboratory testing, vaccination strategy, and reporting strategies. See here for further background and other important considerations surrounding the source data.