On top of each network, we overlay a metapopulation SEIR model that tracks the infection trajectories of each CBG as well as the POIs at which these infections are likely to have occurred. As shown in Supplementary Table 1, POIs are non-residential locations that people visit such as restaurants, grocery stores and religious establishments.
These networks map the hourly movements of 98 million people from census block groups (CBGs), which are geographical units that typically contain 600–3,000 people, to specific points of interest (POIs). To address these needs, we construct fine-grained dynamic mobility networks from mobile-phone geolocation data, and use these networks to model the spread of SARS-CoV-2 within 10 of the largest metropolitan statistical areas (hereafter referred to as metro areas) in the USA. In particular, findings of COVID-19 superspreader events 11, 12, 13, 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 groups 2, 3, 4, 5, 6, 7, 8 require models that can explain the disproportionate effect of the virus on disadvantaged groups. Answering these questions requires epidemiological models that can capture the effects of changes in mobility on virus spread. Since then, public officials have continued to deliberate over when to reopen, which places are safe to return to and how much activity to allow 10. In response to the COVID-19 crisis, stay-at-home orders were enacted in many countries to reduce contact between individuals and slow the spread of the SARS-CoV-2 9. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups 2, 3, 4, 5, 6, 7, 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. Our model predicts that a small minority of ‘superspreader’ points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. 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. Here we introduce a metapopulation susceptible–exposed–infectious–removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. The coronavirus disease 2019 (COVID-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.