1. Epidemiology and Global Health
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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
Research Article
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Cite this article as: eLife 2021;10:e66601 doi: 10.7554/eLife.66601

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.

Reviewing Editor

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

Publication 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)

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. Further reading

Further reading

    1. Epidemiology and Global Health
    William S Hart et al.
    Short Report Updated

    Background:

    Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing.

    Methods:

    Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector–infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches.

    Results:

    The mechanistic method provides an improved fit to data from SARS-CoV-2 infector–infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms.

    Conclusions:

    High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced.

    Funding:

    Engineering and Physical Sciences Research Council (EPSRC).

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    In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory distress syndrome, multi-organ failure, and death. We previously demonstrated that alpha-1 adrenergic receptor (⍺1-AR) antagonists can prevent hyperinflammation and death in mice. Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n = 18,547) and three cohorts with pneumonia (n = 400,907). Federated across two ARD cohorts, we find that patients exposed to ⍺1-AR antagonists, as compared to unexposed patients, had a 34% relative risk reduction for mechanical ventilation and death (OR = 0.70, p = 0.021). We replicated these methods on three pneumonia cohorts, all with similar effects on both outcomes. All results were robust to sensitivity analyses. These results highlight the urgent need for prospective trials testing whether prophylactic use of ⍺1-AR antagonists ameliorates lower respiratory tract infection-associated hyperinflammation and death, as observed in COVID-19.