The Role of Age Distribution and Family Structure on COVID-19 Dynamics: A Preliminary Modeling Assessment for Hubei and Lombardy

21 Pages Posted: 31 Mar 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

Background: The COVID-19 outbreak has already caused significant mortality worldwide. As the epidemic accelerates, understanding the transmission dynamics of COVID-19 is crucial to informing national and regional policies. We develop an individual-level model for SARS-CoV2 transmission which accounts for location-dependent distributions of age and household structure. We apply our model to Hubei, China and Lombardy, Italy to analyze the impact of demographic structure on estimates for key parameters such as the rate of documentation and the reproduction number r0 for COVID-19 cases. We also assess the effectiveness of potential policies ranging from physical distancing to sheltering in place in Lombardy.

Methods: Our study develops a stochastic, agent-based model for SARS-CoV2 spread. A key feature of the model is the inclusion of population-specific demographic structure, such as the distributions of age, household structure, contact across age groups, and comorbidities. We use prior estimates of these demographic features to instantiate our model for two locations: Hubei, China and Lombardy, Italy. Furthermore, we utilize the data on the number of reported deaths due to COVID-19 in both locations to estimate parameters describing location-specific variation in the transmissibility and fatality of the disease (for reasons beyond demography). The range of the parameters in our model that are consistent with reported data are used to construct plausible ranges for r0 and the rate of documentation in each location. Finally, we analyze potential policy responses in the context of Lombardy. Our analysis traces out the trade-off between adoption of physical distancing across the entire population and policies that encourage members of a specific age group to shelter at home.

Results: Our estimates for r0 are comparable to the rest of the literature, with a range of 2.11–2.27 for Hubei and 2.50-3.20 for Lombardy, suggesting higher rates of transmission in the latter. Scenarios where the case fatality rates are higher in Lombardy than Hubei by a factor of 1-5 times appear plausible given the data (even after accounting for differences in age and comorbidity distributions). We estimate the rate at which symptomatic cases are documented to be at 10.3-19.2% in Hubei and 1.2-8% in Lombardy, indicating that the number of undocumented cases may be even higher than has previously been estimated. Evaluation of potential policies suggests that encouraging a single age group to shelter in place is insufficient to control the epidemic by itself, but that targeted "salutary sheltering" by even 50% of a single age group has a substantial impact when combined with adoption of physical distancing by the rest of the population.

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, The Role of Age Distribution and Family Structure on COVID-19 Dynamics: A Preliminary Modeling Assessment for Hubei and Lombardy (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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