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

Author

Listed:
  • Mu-Jeung Yang
  • Maclean Gaulin
  • Nathan Seegert
  • Yang Fan

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.

Suggested Citation

  • Mu-Jeung Yang & Maclean Gaulin & Nathan Seegert & Yang Fan, 2025. "What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-35, May.
    • Handle: RePEc:plo:pone00:0308244
    DOI: 10.1371/journal.pone.0308244
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    References listed on IDEAS

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    2. James H. Stock, 2020. "Data Gaps and the Policy Response to the Novel Coronavirus," NBER Working Papers 26902, National Bureau of Economic Research, Inc.
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    4. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    5. Mu-Jeung Yang & Marinho Bertanha & Nathan Seegert & Maclean Gaulin & Adam Looney & Brian Orleans & Andrew T. Pavia & Kristina Stratford & Matthew Samore & Steven Alder, 2025. "What Is the Active Prevalence of COVID-19?," The Review of Economics and Statistics, MIT Press, vol. 107(1), pages 279-288, January.
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