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

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.

Spatial Clustering of County-Level COVID-19 Rates in the U.S.

“…Specifically, counties that have a higher minority population and lower English language proficiency may be more vulnerable than counties that are predominately White and native English speakers [25]. Furthermore, these relationships have also been suggested by machine learning and other modeling techniques, which have shown a disproportionate burden of COVID-19 infection and mortality among communities of color within the United States [34,35]. Again, structural inequalities likely contribute to these disparities that are apparent in the existing literature and our study.…”

“…There are also studies that have used complex modeling to examine some of the racial/ethnic disparities in COVID-19 rates in the United States. Specifically, researchers used structured compartmental models for seroprevalence data from the state of New York to examine immunity thresholds, final sizes, and COVID-19 risk across groups [34]. Their models suggest that the higher cumulative incidence for Hispanics and non-Hispanic Blacks compared to non-Hispanic Whites reflected the different racial/ethnic inequalities in both individual and community level socioeconomic status indicators [34].…”

“…Specifically, researchers used structured compartmental models for seroprevalence data from the state of New York to examine immunity thresholds, final sizes, and COVID-19 risk across groups [34]. Their models suggest that the higher cumulative incidence for Hispanics and non-Hispanic Blacks compared to non-Hispanic Whites reflected the different racial/ethnic inequalities in both individual and community level socioeconomic status indicators [34]. Additionally, researchers have used machine learning methods to examine the role of racial residential segregation on COVID-19 infection and mortality [35].…”

Mathematical Modeling of Vaccines That Prevent SARS-CoV-2 Transmission

“…Viral load in infected vaccine versus placebo The inability to fully discriminate VE SUSC from VE SYMP , and to directly measure VE INF in the current slate of promising vaccines limits our ability to forecast vaccine impacts in the population. Specifically, there is uncertainty regarding the proportion of vaccinated people required to achieve the herd immunity threshold, where the effective reproductive number (R eff ), given a certain degree of continued social distancing, is maintained below 1 [15]. It is similarly challenging to optimize vaccine allocation to different sectors of the population, particularly when vaccine supply is limited.…”

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk

“…As a result of economic exploitation and inadequate workplace safety, these workers bore much of the brunt of early exposure, infection, and death. This means that lower rates of exposure experienced by wealthier individuals and whites [ 33 ] resulted directly from economic and racialized power imbalances. This pattern, dubbed the “inverse interdependent welfare principle” by the sociologist Erik Olin Wright [ 34 ], has been applied to many problems in social epidemiology [ 35 ] and has clear implications for understanding how policies and social action that shift basic power dynamics can impact infection risks.…”