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 …
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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.
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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.
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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.
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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.
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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...
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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.
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How does lightning not strike you when you tweet this
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