In defence of armchair epidemiology

OK, "Flatten the curve of armchair epidemiology" was very funny; and "Ten Considerations Before You Create Another Chart About COVID-19" makes some important points about avoiding both panic and indifference; as does Slate's "Stop the epidemic of armchair epidemiology".

But we armchair epidemiologists, we unsophisticated sirens of social media, Excel crusaders and lackadaisical luminaries of LinkedIn; we too have a role to play.

He who can, does. He who can not teaches*

Professional epidemiologists are busy building models to understand how quickly COVID-19 spreads, what measures will work, how long we will need them; how many hospital beds we will need and how many respirators.

In the meantime, the world's population is enduring the excruciating economic repercussions of the momentous measures these models tell us are better than the biblical alternative of letting the contagion rage.

With so many of the public at large at the mercy of the mandate of a handful of mathematical models, the public understanding of those models has never been more essential.

Pedagogic epidemiology is a civic duty.

A learned man is an idler who kills time with study. Beware of his false knowledge: it is more dangerous than ignorance

My next 'blog post is entitled "What I know I don't know"** In it, I describe developments to the very simple model (SIR) I used in my last 'blog post, where I explain some of the terms we hear so much about in the media: exponential growth, flattening the curve, herd immunity, etc.

The purpose of describing these developments is to introduce and begin to explain some of the models that are becoming influential with policy makers and doing the rounds on the internet (not, unfortunately, always in that order).

Some of these are only marginal improvements on SIR, like the SEIR model that is the basis of Gabriel Goh's Epidemic calculator (right), which is getting a lot of use, amongst other places with the very influential Tomas Puyeo. Others represent the pinnacle of epidemiological probity, like the WHO's study on clinical severity, based on data from the Wuhan outbreak, and the Imperial College response team's study, which I reviewed in a previous 'blog post.

My motivation for this presentation of more advanced modelling was to help people understand the research that was so influential in the form of their everyday lives at the moment. Then I realized an uncanny resemblance between my gimcrack Excel SEIR model and some of the curves being shown by health policy experts and suddenly the post acquired the additional motivation of spreading the word on the state of the art and why those developments are so important.

Activity is the only road to knowledge.

My appeal to my fellow armchair epidemiologists is to model, but not make models your aim. Build your own models to understand the models of others and then spread that understanding. And don't let the naysayers get you down.

* The section headings in this 'blog post are all taken from George Bernard Shaw's Maxims for Revolutionists.

** The new 'blog post "What I know I don't know" should drop later in the week. One of the first casualties of lockdown is that grown men and women start using words like "drop" because they've been stuck at home with their teenage children for several weeks.