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PLoS One
. 2025 May 12;20(5):e0308244.
doi: 10.1371/journal.pone.0308244. eCollection 2025. What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?
Mu-Jeung Yang 1 , Maclean Gaulin 2 , Nathan Seegert 2 , Yang Fan 3
Affiliations
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.
. 2025 May 12;20(5):e0308244.
doi: 10.1371/journal.pone.0308244. eCollection 2025. What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?
Mu-Jeung Yang 1 , Maclean Gaulin 2 , Nathan Seegert 2 , Yang Fan 3
Affiliations
- PMID: 40354357
- DOI: 10.1371/journal.pone.0308244
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.