Mobility network models of COVID-19 explain inequities and inform reopening
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Abstract
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. 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. 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 SEIR 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 of the 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 groups 2-8 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 COVID-19.
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Strategies to reopen an economy from lockdowns have been studied using check-in data, Wi-Fi association logs and GPS traces.
“Strategies to reopen an economy from lockdowns has been studied [106,107] using check-in data, Wi-Fi association logs and GPS traces.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [106]
To reduce disease spread and retain social activities, reopening strategies need to consider the mobility of the people, for example, protect the most mobile people or restricting large gatherings.
“The primary conclusions are that to reduce the disease spread and retain social activities, reopening strategies need to consider the mobility of the people, for example, protect the most mobile people or restricting large gatherings.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [106]
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- Authors
- Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J.
- Affiliations
- Stanford University (US)
- Microsoft Research New England (United States) (US)
- Northwestern University (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Leskovec, Jure
- ORCID
- Venue
- Nature
- Published
- 2020-11-10
- Volume
- 589
- Issue
- 7840
- Pages
- 82-87
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3099011804
- Cited by
- 1,533
- References
- 53
- Citations by year
- 2026: 92 · 2025: 129 · 2024: 191 · 2023: 250 · 2022: 362 · 2021: 457 · 2020: 50 · 2016: 1
- FWCI
- 44.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1934578, 1835598, 1918940, 2030477)
- Wu Tsai Neurosciences Institute, Stanford University
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- COVID-19 pandemic, SARS-CoV-2 transmission, mobility networks, SEIR model, metapopulation model, mobile phone data, superspreading, points of interest, socioeconomic inequities, racial disparities, urban mobility
- MeSH terms
- Data Analysis, Physical Distancing, COVID-19, Computer Simulation, Humans, Locomotion, Religion, Restaurants, Socioeconomic Factors, Time Factors, Risk Assessment, Cell Phone, Racial Groups, Mobile Applications
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