Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City
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2010651117.full.pdf
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6.1 MB
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Author(s)
Wilder, Bryan
Charpignon, Marie-Laure
Killian, Jackson A.
Ou, Han-Ching
Mate, Aditya
Jabbari, Shahin
Perrault, Andrew
Desai, Angel N.
Tambe, Milind
Majumder, Maimuna S.
Date Issued
September 2020
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Citation
Wilder, Bryan et al. "Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City." Proceedings of the National Academy of Sciences (September 2020): dx.doi.org/10.1073/pnas.2010651117
Version
Final published version
Abstract
As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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DOI of Published Version
https://doi.org/10.1073/pnas.2010651117
