What drives the effectiveness of social distancing in combating COVID-19 across U.S. states? - Mu-Jeung Yang; Maclean Gaulin; Nathan Seegert; Yang Fan (PLoS ONE, 2025)
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.