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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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    1. James H. Stock, 2020. "Data Gaps and the Policy Response to the Novel Coronavirus," NBER Working Papers 26902, National Bureau of Economic Research, Inc.
    2. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    3. Goolsbee, Austan & Syverson, Chad, 2021. "Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020," Journal of Public Economics, Elsevier, vol. 193(C).
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