  Understanding where and when human mobility is associated with disease
infection is crucial for implementing location-based health care policy and
interventions. Previous studies on COVID-19 have revealed the correlation
between human mobility and COVID-19 cases. However, the spatiotemporal
heterogeneity of such correlation is not yet fully understood. In this study,
we aim to identify the spatiotemporal heterogeneities in the relationship
between human mobility flows and COVID-19 cases in U.S. counties. Using
anonymous mobile device location data, we compute an aggregate measure of
mobility that includes flows within and into each county. We then compare the
trends in human mobility and COVID-19 cases of each county using dynamic time
warping (DTW). DTW results highlight the time periods and locations (counties)
where mobility may have influenced disease transmission. Also, the correlation
between human mobility and infections varies substantially across geographic
space and time in terms of relationship, strength, and similarity.
