Coronavirus Tips & Models — Social-Science Lessons from COVID-19

The coronavirus pandemic has thrust data-wranglers into a national spotlight they normally enjoy only in the runup to a presidential election. The death projections put forth by the University of Washington’s COVID-19 model have become the New York Times election needle of early to mid 2020: We all hang on their every fluctuation. Other recent developments have treated the public to discussions about statistical concepts ranging from “sensitivity and specificity” to “selection bias” to “quality-adjusted life years.”

For those who haven’t been following this obsessively — what, you have better things to do? — here’s a quick guide to the key developments.

About the Author

Robert VerBruggen

Robert VerBruggen is a senior fellow at the Manhattan Institute.

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The coronavirus pandemic has thrust data-wranglers into a national spotlight they normally enjoy only in the runup to a presidential election. The death projections put forth by the University of Washington’s COVID-19 model have become the New York Times election needle of early to mid 2020: We all hang on their every fluctuation. Other recent developments have treated the public to discussions about statistical concepts ranging from “sensitivity and specificity” to “selection bias” to “quality-adjusted life years.”

For those who haven’t been following this obsessively — what, you have better things to do? — here’s a quick guide to the key developments.