Number of confirmed coronavirus cases per country, on a linear-logarithmic scale.

Basic reproduction number of well-known infectious diseases. (Source: Wikipedia)

As an additional benefit, linear-logarithmic plots like these can educate the general public about the exponential nature of the disease in its early stage. This avoids more sensation-oriented headlines such as ‘More new infections today than ever before!’. While this is true, it doesn’t have to mean things are getting out of control. Everything might be entirely as expected, or even improving. As Hans Rosling notes in his bestseller Factfulness (if you haven’t read it, do it immediately!): things can be both better and bad.

A final note on logarithmic scales before I shut up about it and we can move on to more exciting things. As the infection continuous to spread, a growing fraction of the population will become either already infected, or immune when they have been infected in the past but survived. Also, vaccines can be developed, or increasingly strict measures of social distancing and quarantine can be enforced. In practice, this means the effective reproduction number will drop, the exponential growth will start to decelerate and the number of infections will reach a peak and start dropping again. When this happens, the usefulness of logarithmic scales has reached its end.

Even Bill agrees.

By now, I have created many different visuals related to the novel coronavirus, and one of the most pervasive comments concerns the use of absolute numbers. Many people argue that we cannot directly compare numbers between countries without correcting for the population count. This sounds relatively convincing — how on earth can we compare the number of deaths between a tiny country such as Belgium, and a massive nation like China?

However, this is a multifaceted question without a definitive answer. Let me list the major arguments for both approaches:

We must use relative numbers, because:

• The number of infections and deaths in a country depends on the size of that country.
• We want to evaluate the stress the pandemic will put on a country’s healthcare system, which can usually only support a certain fraction of the population being infected.

We must use absolute numbers, because:

• The rate at which a disease spreads depends on the population density and the level of social distancing, but has nothing to do with the population number. In this regard, country borders are pretty arbitrary ways of grouping people, anyway.
• Relative numbers will create pretty meaningless ‘outliers’ for some very small countries.
• Should we plot the relative or the absolute number of infections?

In my impression, most data visualizers follow the approach of using absolute numbers when plotting the total number of infections. The above-mentioned John Burn-Murdoch also agrees:

Thanks for backing me up here, John!

Nevertheless, using relative numbers can be very useful for other use cases such as:

• Showing the number of tests performed per capita.
• Showing the available number of doctors, hospital beds, intensive care beds,… per capita.
• Comparing how hard different countries are currently hit by the crisis.

For example, comparing the number of COVID-19 tests performed by country in absolute and relative numbers reveals some interesting insights:

Nevertheless, using relative numbers can be very useful for other use cases such as:

• Showing the number of tests performed per capita.
• Showing the available number of doctors, hospital beds, intensive care beds,… per capita.
• Comparing how hard different countries are currently hit by the crisis.

For example, comparing the number of COVID-19 tests performed by country in absolute and relative numbers reveals some interesting insights:

Number of tests performed per country and per 1.000 inhabitants. (Source: Our World in Data)