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While several non-pharmacological measures have been implemented for a fewmonths in an effort to slow the coronavirus disease (COVID-19) pandemic in theUnited States, the disease remains a danger in a number of counties asrestrictions are lifted to revive the economy. Making a trade-off betweeneconomic recovery and infection control is a major challenge confronting manyhard-hit counties. Understanding the transmission process and quantifying thecosts of local policies are essential to the task of tackling this challenge.Here, we investigate the dynamic contact patterns of the populations fromanonymized, geo-localized mobility data and census and demographic data tocreate data-driven, agent-based contact networks. We then simulate the epidemicspread with a time-varying contagion model in ten large metropolitan countiesin the United States and evaluate a combination of mobility reduction, maskuse, and reopening policies. We find that our model captures thespatial-temporal and heterogeneous case trajectory within various countiesbased on dynamic population behaviors. Our results show that a decision-makingtool that considers both economic cost and infection outcomes of policies canbe informative in making decisions of local containment strategies for optimalbalancing of economic slowdown and virus spread.

Data-driven Contact Network Models of COVID-19 Reveal Trade-offs between Costs and Infections for Optimal Local Containment Policies