Understanding the Numbers Behind COVID-19

Predicting an epidemic requires understanding a few simple metrics.

As you know, in this forum I usually talk about a new study with a focus on the data and its broader implications. This week, I want to take a bit of a deep dive into how infectious disease epidemiology works, using, of course, the novel 2019 coronavirus as a great real-time example.

You’ve heard a ton about the new virus over the past month — reports in top medical journals detailing case series, breathless newscasters asking if this is the next Spanish flu, and of course some cautious statements from government officials charged with containing the pandemic.

All of those reports focus on numbers — cases, incubation periods, attack rates, fatality rates, basic reproduction number. But I think as healthcare providers we need to have some better intuition about what these numbers really mean, how they fit in with other infectious diseases that are more familiar to us, and importantly — how they can be misestimated. Because the vagaries in these estimates can make the difference between a flash-in-the-pan scare and full-blown worldwide panic.

Ok let’s start with the big one — the basic reproduction number, or R0. This represents the average number of susceptible individuals an infected…