Putting Covid-19 into perspective

Here’s another way of visualising the numbers connected with the coronavirus.

Just how contagious is COVID-19? This chart puts it in perspective – Popular Science
One quantity scientists use to measure how a disease spreads through a population is the “basic reproduction number,” otherwise known as R0 (pronounced “R naught,” or, if you hate pirates, “arr not”). This number tells us how many people, on average, each infected person will in turn infect. While it doesn’t tell us how deadly an epidemic is, R0 is a measure of how infectious a new disease is, and helps guide epidemic control strategies implemented by governments and health organizations.

If R0 is less than 1, the disease will typically die out: Each infected person has a low chance of passing the infection along to even one additional individual. An R0 larger than 1 means each sick person infects at least one other person on average, who then could infect others, until the disease spreads through the population. For instance, a typical seasonal flu strain has an R0 of around 1.2, which means for every five infected people, the disease will spread to six new people on average, who pass it along to others.

Here’s more on that.

What is the coronavirus’s R0 and why does it matter? – Life Hacker
R0 is one of the numbers epidemiologists use to describe how an infectious agent spreads through a population. But it’s important to remember that it’s simply a statistic that describes some of the numbers we see. It’s not a rating of how scary a virus is, nor does it dictate how deadly a disease is or how difficult it might be to contain. We need more information for that.

And another way of comparing such things, from 2014.

Visualised: how Ebola compares to other infectious diseases – The Guardian
Every disease has a basic reproduction number but the numbers are scattered across the literature. We’ve web-crawled and gathered them all here in one graphic, plotting them against the average case fatality rate – the % of infectees who die. This hopefully gives us a data-centric way to understand the most infectious and deadly diseases and contextualise current events.

Author: Terry Madeley

Works with student data and enjoys reading about art, data, education and technology. View all posts by Terry Madeley