Friday, March 13, 2020

Explaining and simulating the coronavirus

The other day a reporter asked me about my favorite visualizations about the coronavirus. I've been hesitant about the quality of many graphics I've seen (thisthis, this, and this,) so I chose the “flatten the curve” abstract diagram—many of its versions can't be called data visualizations, as they don't encode actual data—particularly when adding a verbal annotation layer to it, like CNN's Brian Stelter did. Stelter acted like the “mediators” I discussed in my recent article for IEEE. We shouldn't just show information to viewers or readers; we ought to explain it.

Nicholas Kristof and Stuart Thompson have just released another intriguing piece. This one lets you simulate how the curve would change depending on how early or late you intervene to stop the spread of the virus, or how mild or aggressive your actions are:

In any case, here's my take about visualizing anything related to the coronavirus—or anything at all, for that matter: don't mindlessly apply your generic statistical or visualization skills to data downloaded from public sources when covering serious topics; you likely lack domain-specific knowledge, which is essential to getting things right. Always consult with an expert or two. Seek the help of epidemiologists, biostatisticians, or public health specialists. And, when in doubt, err on the side of caution and don't publish anything.

UPDATE: I'd revisit this 2017 article by Steve Wexler and Jeff Shaffer: “Publishing bogus findings undermines our credibility. It suggests we value style over substance, that we don’t know enough to relentlessly question our data sources.”