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Mobility network models of COVID-19 explain inequities and inform reopening


The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore 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 COVID-19.

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Correspondence to Jure Leskovec.

Supplementary information

Supplementary Information

The Supplementary Information contains four sections: Supplementary Methods, Supplementary Discussion, Supplementary Tables, and Supplementary Figures. The Supplementary Methods section describes (1) our comparison of SafeGraph mobility data to Google mobility data, where we find high correlation; (2) sensitivity analyses of the model (e.g., modifying CBG and POI transmission rates, fitting to deaths instead of cases) and tests of model identifiability, (3) a detailed description of how we estimated dynamic mobility networks from raw SafeGraph data using iterative proportional fitting. The Supplementary Discussion section covers (1) further discussion of the racial and socioeconomic disparities predicted by our model; (2) limitations of the model and mobility dataset. There are 6 Supplementary Tables, which provide more details about the SafeGraph data, the comparison to Google mobility data, and additional model results. There are 24 Supplementary Figures, which include additional results about predicted disparities, results from all sensitivity analyses and identifiability checks, and POI attributes and predicted reopening risks for every POI category and metro area.

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Chang, S., Pierson, E., Koh, P.W. et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature (2020).

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