What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?

Mu-Jeung Yang, Maclean Gaulin, Nathan Seegert and Yang Fan

PLOS ONE, 2025, vol. 20, issue 5, 1-35

Abstract: We propose a new theory of information-based voluntary social distancing in which people’s responses to disease prevalence depend on the credibility of reported cases and fatalities and vary locally. We embed this theory into a new pandemic prediction and policy analysis framework that blends compartmental epidemiological/economic models with Machine Learning. We find that lockdown effectiveness varies widely across US States during the early phases of the COVID-19 pandemic. We find that voluntary social distancing is higher in more informed states, and increasing information could have substantially changed social distancing and fatalities.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0308244

DOI: 10.1371/journal.pone.0308244

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