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

Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping