Modeling Between-Population Variation in COVID-19 Dynamics in Hubei, Lombardy, and New York City

23 Pages Posted: 31 Mar 2020 Last revised: 29 May 2020

See all articles by Bryan Wilder

Bryan Wilder

Harvard University - Center for Research on Computation and Society

Marie Charpignon

Massachusetts Institute of Technology (MIT)

Jackson A Killian

Harvard University - Center for Research on Computation and Society

Han-Ching Ou

Harvard University - Center for Research on Computation and Society

Aditya Mate

Harvard University - Center for Research on Computation and Society

Shahin Jabbari

Harvard University - Center for Research on Computation and Society

Andrew Perrault

Harvard University - Center for Research on Computation and Society

Angel Desai

International Society for Infectious Diseases

Milind Tambe

Harvard University - Center for Research on Computation and Society

Maimuna S. Majumder

Boston Children's Hospital - Computational Health Informatics Program; Harvard University - Harvard Medical School

Date Written: March 31, 2020

Abstract

As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential SARS-CoV2 transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV2 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.

Code may be found at https://github.com/bwilder0/covid_abm_release.

Note: Funding: This work was supported in part by the Army Research Office by grant MURI W911NF1810208 and in part by grant T32HD040128 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. Killian was supported by the National Science Foundation Graduate Research Fellowship by grant DGE1745303. Perrault and Jabbari were supported by the Harvard Center for Research on Computation and Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of Interest: There is no competing interest.

Keywords: COVID-19, modelling, agent-based model, demographics, physical distancing

Suggested Citation

Wilder, Bryan and Charpignon, Marie and Killian, Jackson and Ou, Han-Ching and Mate, Aditya and Jabbari, Shahin and Perrault, Andrew and Desai, Angel and Tambe, Milind and Majumder, Maimuna, Modeling Between-Population Variation in COVID-19 Dynamics in Hubei, Lombardy, and New York City (March 31, 2020). Available at SSRN: https://ssrn.com/abstract=3564800 or http://dx.doi.org/10.2139/ssrn.3564800

Bryan Wilder (Contact Author)

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Marie Charpignon

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Jackson Killian

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Han-Ching Ou

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Aditya Mate

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Shahin Jabbari

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Andrew Perrault

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Angel Desai

International Society for Infectious Diseases ( email )

Brookline, MA

Milind Tambe

Harvard University - Center for Research on Computation and Society ( email )

33 Oxford Street
Cambridge, MA 02138
United States

Maimuna Majumder

Boston Children's Hospital - Computational Health Informatics Program ( email )

United States

Harvard University - Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

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