The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention.

This article presents a holistic, data-informed modeling approach developed to jointly analyze human mobility, health risk, and economic activity during the COVID-19 pandemic, aiming to balance public health objectives with economic considerations. The core innovation is an individual-level susceptible-infected-recovered (SIR) epidemiological model that integrates granular mobility data, socio-demographic factors, and points-of-interest (POI) categories to more accurately predict COVID-19 spread and assess health risks, coupled with an economic choice model capturing consumers' decisions between online and offline shopping influenced by infection risk, delivery fees, distance, and income. The study uses proprietary large-scale data sets—including mobile phone location data, bank transaction records, and public health case data—from Pennsylvania to calibrate and validate the models, demonstrating improved predictive performance over traditional aggregate models. Counterfactual policy simulations reveal that delivery fee subsidies can reduce infection rates, especially benefiting low-income groups; mask mandates may reduce infections only under strict enforcement due to behavioral risk compensation; and categorical lockdowns lower infection rates but with diminished effectiveness when shifts to online shopping are considered. While focused on consumer behavior, the approach is generalizable for evaluating diverse policy interventions and highlights the complex feedback loops between mobility, economic activity, and epidemic dynamics.