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

TL;DR

This study develops data-driven contact network models using mobility and demographic data to simulate COVID-19 spread and evaluate policies balancing economic costs and infection control in US counties.

Contribution

It introduces a novel agent-based modeling approach that incorporates dynamic population behaviors and evaluates combined containment strategies.

Findings

Model accurately captures spatial-temporal infection patterns.

Combining mobility reduction and mask use effectively reduces infections.

Decision tools balancing costs and infections aid local policy-making.

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 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.