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

Elife. 2021 May 18:10:e66601. doi: 10.7554/eLife.66601.


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 change 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.

Keywords: COVID-19; Herd immunity; SARS-CoV-2; epidemiology; global health; mathematical modeling; viruses.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asian People / statistics & numerical data
  • Black or African American / statistics & numerical data
  • COVID-19 / blood
  • COVID-19 / diagnosis
  • COVID-19 / epidemiology*
  • COVID-19 / immunology
  • Cost of Illness
  • Health Status Disparities*
  • Hispanic or Latino / statistics & numerical data
  • Humans
  • Immunity, Herd
  • Minority Groups / statistics & numerical data
  • Models, Statistical*
  • New York / epidemiology
  • Pandemics / statistics & numerical data*
  • SARS-CoV-2 / immunology
  • SARS-CoV-2 / isolation & purification
  • Seroepidemiologic Studies
  • White People / statistics & numerical data