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

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