Changes to data format occasionally result in some portions of these visuals being unavailable - we aim to reduce the downtime of these missing portions as much as possible. Planned changes on 30 November resulted in some locations' data no longer being available from OWID. These will be reinstated as and when possible. If a location does not appear in the visualisations, it may be considered a "dependent territory" and its COVID-19 activity has been aggregated with a sovereign nation such as Australia, China, Denmark, Finland, France, Netherlands, New Zealand, Norway, the United Kingdom or the United States.
The breadth and depth of data visualisation offered by the team at Our World in Data has continued to expand and now includes a data explorer. The team has also incorporated the data collected by the Blavatnik School of Government tracking of the international responses to the pandemic.
The following visualisations have been created directly by the team at Our World In Data, using the same data from Johns Hopkins University. Some data from the Blavatnik School of Government is also included below.
In the charts below:
In the chart below, the angle of the slope of cases represents how rapidly they are increasing. The initial slopes of the graphs has been set from the time each country reported the 100th case. As shown, almost all countries rate of doubling has slowed significantly with a few still doubling every 10 days or so.
In comparing the rising number of cases reported in different countries, it is important to consider the number of tests being carried out. Put simply, if you don't look for the disease, you don't know if it is there or not. Charts describing the number of tests performed by each country include comparisons between the number of cases against the number of tests performed. Again, the axes can be toggled between logarithmic (default) and linear scales.
When considering comparisons between countries of widely varying populations, it is good practice to "normalise" the comparisons by dividing the raw data by the population size to get a more accurate representation of the data.
In the graph below, the number of tests carried out is normalised to count per 1000 people in the population. This graph shows the cumulative tests carried out per 1000 people in different countries. You can use Add country to compare those locations of interest. Smaller populations may appear to carry out more testing than larger populations in the early stages of the outbreak because a small increase in tests carried out means a larger proportion of their population has been tested than if the same number of tests were carried out in countries with much larger populations.
The variation in governmental responses has also been tracked by Oxford University, by the Blavatnik School of Government. These visuals chart the main domains monitored by them and explores each theme in turn and the resulting overall index is shown in the last map.