Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies.

ABSTRACT: 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 m

SUBMITTER: Fan C 

PROVIDER: S-EPMC9174357 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature