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. M.L. was supported by SeroNet cooperative agreement U01 CA261277.

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

SeroNet (cooperative agreement U01 CA261277)

  • Marc Lipsitch

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

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.

Metrics

  • 2,417
    views
  • 353
    downloads
  • 25
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Share this article

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

Further reading

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Rashmi Sukumaran, Achuthsankar S Nair, Moinak Banerjee
    Research Article

    Burden of stroke differs by region, which could be attributed to differences in comorbid conditions and ethnicity. Genomewide variation acts as a proxy marker for ethnicity, and comorbid conditions. We present an integrated approach to understand this variation by considering prevalence and mortality rates of stroke and its comorbid risk for 204 countries from 2009 to 2019, and Genome-wide association studies (GWAS) risk variant for all these conditions. Global and regional trend analysis of rates using linear regression, correlation, and proportion analysis, signifies ethnogeographic differences. Interestingly, the comorbid conditions that act as risk drivers for stroke differed by regions, with more of metabolic risk in America and Europe, in contrast to high systolic blood pressure in Asian and African regions. GWAS risk loci of stroke and its comorbid conditions indicate distinct population stratification for each of these conditions, signifying for population-specific risk. Unique and shared genetic risk variants for stroke, and its comorbid and followed up with ethnic-specific variation can help in determining regional risk drivers for stroke. Unique ethnic-specific risk variants and their distinct patterns of linkage disequilibrium further uncover the drivers for phenotypic variation. Therefore, identifying population- and comorbidity-specific risk variants might help in defining the threshold for risk, and aid in developing population-specific prevention strategies for stroke.

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Renan Maestri, Benoît Perez-Lamarque ... Hélène Morlon
    Research Article

    Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus–host codiversification have been largely over-estimated.