Mobile Device Location Data Reveals Human Mobility Response to Stay-at-Home Orders during the COVID-19 Pandemic in the U.S.

Abstract: One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study utilizes an integrated dataset, consisting of anonymized and privacy-protected location data that covers over 150 million monthly active samples in the U.S., COVID-19 case data, and census population information, to uncover mobility changes during COVID-19 and under the “Stay-at-home” state orders in the U.S. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. The data analytics reveal a voluntary mobility reduction that occurred regardless of government actions, and a “floor” phenomenon that human mobility reached a lower bound and stopped decreasing soon after each state announced the “Stay-at-home” order. A set of longitudinal models is then developed and confirms empirically that about 5% of the reduction in human mobility is due to the effect of states’ “Stay-at-home” policy. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 or other virus outbreak in the future.