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

Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S