Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics

  1. Kevin C Ma  Is a corresponding author
  2. Tigist F Menkir
  3. Stephen M Kissler
  4. Yonatan H Grad
  5. Marc Lipsitch
  1. Harvard TH Chan School of Public Health, United States

Abstract

Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS CoV-2 spread is largely unknown.

Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk changes across groups.

Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites.

Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection.

Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following previously published data sets were used

Article and author information

Author details

  1. Kevin C Ma

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    For correspondence
    kevinchenma@g.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4326-2911
  2. Tigist F Menkir

    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  3. Stephen M Kissler

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3062-7800
  4. Yonatan H Grad

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5646-1314
  5. Marc Lipsitch

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    Marc Lipsitch, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1504-9213

Funding

National Science Foundation (DGE1745303)

  • Kevin C Ma

Morris-Singer Foundation

  • Yonatan H Grad
  • Marc Lipsitch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Joshua T Schiffer, Fred Hutchinson Cancer Research Center, United States

Version history

  1. Received: January 15, 2021
  2. Accepted: May 17, 2021
  3. Accepted Manuscript published: May 18, 2021 (version 1)
  4. Accepted Manuscript updated: May 24, 2021 (version 2)
  5. Version of Record published: June 23, 2021 (version 3)

Copyright

© 2021, Ma et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kevin C Ma
  2. Tigist F Menkir
  3. Stephen M Kissler
  4. Yonatan H Grad
  5. Marc Lipsitch
(2021)
Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics
eLife 10:e66601.
https://doi.org/10.7554/eLife.66601

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https://doi.org/10.7554/eLife.66601

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