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The coronavirus disease 2019 (COV1D-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptibleexposed-infectious-removed (SE1R) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SE1R model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority ofthe infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COV1D-19.
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In response to the COV1D-19 crisis, stay-at-home orders were enacted in many countries to reduce contact between individuals and slow the spread of the SARS-CoV-29. Since then, public officials have continued to deliberate over when to reopen, which places are safe to return to and how much activity to allow10. Answering these questions requires epidemiological models that can capture the effects of changes in mobility on virus spread. 1n particular, findings of COV1D-19 superspreader events11-14 motivate models that can reflect the heterogeneous risks of visiting different locations, whereas well-reported disparities in infection rates among different racial and socioeconomic groups2-8 require models that can explain the disproportionate effect of the virus on...