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One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. It is yet unclear how effective those policies are on suppressing the mobility trend due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study uses real-world location-based service data collected from anonymized mobile devices to uncover mobility changes during COVID-19 and under the 'Stay-at-home' state orders in the U.S. The study measures human mobility with two important metrics: daily average number of trips per person and daily average person-miles traveled. The data-driven analysis and modeling attribute less than 5% of the reduction in the number of trips and person-miles traveled to the effect of the policy. The models developed in the study exhibit high prediction accuracy and can be applied to inform epidemics modeling with empirically verified mobility trends and to support time-sensitive decision-making processes.

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 observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from 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 travelled and daily percentage of residents staying home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions, and a ‘floor’ phenomenon, where 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 that the states' ‘stay-at-home’ policy has only led to about 5% reduction in average daily human mobility.Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge, or another virus outbreak in the future.