  While several non-pharmacological measures have been implemented for a few
months in an effort to slow the coronavirus disease (COVID-19) pandemic in the
United States, the disease remains a danger in a number of counties as
restrictions are lifted to revive the economy. Making a trade-off between
economic recovery and infection control is a major challenge confronting many
hard-hit counties. Understanding the transmission process and quantifying the
costs of local policies are essential to the task of tackling this challenge.
Here, we investigate the dynamic contact patterns of the populations from
anonymized, geo-localized mobility data and census and demographic data to
create data-driven, agent-based contact networks. We then simulate the epidemic
spread with a time-varying contagion model in ten large metropolitan counties
in the United States and evaluate a combination of mobility reduction, mask
use, and reopening policies. We find that our model captures the
spatial-temporal and heterogeneous case trajectory within various counties
based on dynamic population behaviors. Our results show that a decision-making
tool that considers both economic cost and infection outcomes of policies can
be informative in making decisions of local containment strategies for optimal
balancing of economic slowdown and virus spread.
