Mobility network modeling explains higher SARS-CoV-2 infection rates among disadvantaged groups and informs reopening strategies

We model the spread of SARS-CoV-2 using a dynamic mobility network that encodes the hourly movements of millions of people between neighborhoods and points of interest, and use this model to study the equity and efficiency of fine-grained reopening strategies.


In response to the SARS-CoV-2 crisis, numerous stay-at-home orders were enacted across the United States in order to reduce contact between individuals and slow the spread of the virus. As of May 2020, these orders are being relaxed, businesses are beginning to reopen, and mobility is increasing, causing concern among public officials about the potential resurgence of cases. Epidemiological models that can capture the effects of changes in mobility on virus spread are a powerful tool for evaluating the effectiveness and equity of various strategies for reopening or responding to a resurgence. In particular, findings of SARS-CoV-2 "super-spreader" events motivate models that can reflect the heterogeneous risks of visiting different locations, while well-reported racial and socioeconomic disparities in infection rates require models that can explain the disproportionate impact of the virus on disadvantaged demographic groups.


Our main methodological innovation is overlaying a SEIR metapopulation disease model on a fine-grained mobility network that captures the hourly movements of millions of people from local neighborhoods (census block groups, or CBGs) to points of interest (POIs), which we estimate using highly-detailed cell phone geolocation data. In our model, transmission dynamics for each census block group are determined by the POIs that its residents visit, how long they stay at each POI, and how crowded the POI is when they visit.

Please refer to our paper for detailed explanations and more results.


Python code to reproduce our experiments is in preparation and will be made publicly available.


The following people contributed to this work:
Serina Chang*
Emma Pierson*
Pang Wei Koh*
Jaline Gerardin
Beth Redbird
David Grusky
Jure Leskovec