  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.
