So, here's a podcast with @brianstelter where I talk about media coverage of coronavirus data. I think media coverage of coronavirus overall has been good in many respects, but there are basically three things that frustrate me from a data point of view. https://twitter.com/brianstelter/status/1261855220168081409

1. I think there's not nearly enough recognition in the media that the data we have on coronavirus is highly imperfect and this sometimes leads to misleading conclusions.

e.g. experts estimate that only ~10% of coronavirus *infections* have become detected *cases* in the US. So accounting for how well we're doing at detection (e.g. test volume) is very important.

Deaths are also significantly underreported, more so in some contexts than others.

There are a lot of other quirks in reporting. The data is proceeded by humans; it doesn't appear automatically. Lags are unavoidable, but some data series are more lagging than others. Many places have "lumpy" data, i.e. they report data in batches rather than in a steady flow.

2. The next major category is not accounting enough for uncertainty. I actually think the experts have done a very good job of accounting for the major traits of COVID-19, especially if you (literally) read the fine print on what they were saying rather through the media filter.

But it's a new disease! there's a lot we don't know! The experts are learning as they go! While there are some places where we should have relatively strong priors, in general experts need space to revise their conclusions and the media should be attentive to those revisions...

Which means often reporting conclusions as provisional or "the best evidence available right now". It also means avoiding overly confident predictions and proclamations and avoiding the impulse to weave everything into a neatly-packaged narrative.

3. Finally—I think the media spends too much time worrying about knock-off effects of their coverage, i.e. worrying about scaring people or about making them complacent, and should instead try to report the facts as straightforwardly as possible, including the many uncertainties.

For one thing, that is simply the professional obligation of journalists, in my view. I'm a traditionalist in this respect. Report the facts. Pay attention to the quality of your writing (e.g. avoid confusing jargon). But don't try to influence readers' behavior.

Or if you DO want to influence behavior, make that transparent. Nothing wrong IMO with writing an editorial saying "HERE'S WHY YOU SHOULD WEAR A MASK". Just make clear that's what you're doing, instead of infusing it into your reporting of straight news stories.

But also, it's fairly common for attempts to influence behavior to backfire and sometimes to produce the opposite behavior from what was intended. Maybe I'll do a separate thread sometime this week with examples of this.

One salient example is dramatic headlines and predictions put out around re-opening plans in states such as GA and TX a few weeks ago, which sometimes seemed written to bolster support for maintaining distancing or to scare these states into shape.

Maybe those headlines will turn out to be true. There are lags, so let's definitely give it another week or two. But *so far* the evidence is mixed. And it's easy enough to see a path where those dramatic predictions/headlines backfire and wind up reducing support for distancing.

The point is: this is a long game. COVID-19 will be with us for a while. Maybe if we're very smart/lucky, it will have become less of a problem by the fall or by next spring. But those are optimistic scenarios. It could take years. So maintaining reader trust will be essential.