Quantifying the influence of inter-county mobility patterns on the COVID-19 outbreak in the United States

TL;DR

This study analyzes how inter-county mobility patterns in the U.S., derived from mobile device data, influence COVID-19 spread, providing insights to improve intervention strategies.

Contribution

It introduces a novel log-linear double-risk model that quantifies external and internal risks of COVID-19 spread at the county level using mobility data.

Findings

Inter-county mobility significantly correlates with COVID-19 case distribution.

The model identifies high-risk counties based on mobility and vulnerability.

Mobility patterns from New York City impact other counties' outbreak dynamics.

Abstract

As a highly infectious respiratory disease, COVID-19 has become a pandemic that threatens global health. Without an effective treatment, non-pharmaceutical interventions, such as travel restrictions, have been widely promoted to mitigate the outbreak. Current studies analyze mobility metrics such as travel distance; however, there is a lack of research on interzonal travel flow and its impact on the pandemic. Our study specifically focuses on the inter-county mobility pattern and its influence on the COVID-19 spread in the United States. To retrieve real-world mobility patterns, we utilize an integrated set of mobile device location data including over 100 million anonymous devices. We first investigate the nationwide temporal trend and spatial distribution of inter-county mobility. Then we zoom in on the epicenter of the U.S. outbreak, New York City, and evaluate the impacts of its outflow on other counties. Finally, we develop a "log-linear double-risk" model at the county level to quantify the influence of both "external risk" imported by inter-county mobility flows and the "internal risk" defined as the vulnerability of a county in terms of population with high-risk phenotypes. Our study enhances the situation awareness of inter-county mobility in the U.S. and can help improve non-pharmaceutical interventions for COVID-19.