How should infectious disease models tackle infection inequity?

Jon Zelner
[email protected]

Dept. of Epidemiology
Center for Social Epidemiology and Population Health (CSEPH)
University of Michigan School of Public Health

EpiBayes Research Group


  • How did we get here?

  • How can we make modeling a useful tool for advancing health equity?

  • What challenges remain?

How did we get here?

From the beginning, influential COVID-19 models have focused on key priorities

  • Spatial variation in risks of infection and death.
  • Age-specific incidence and mortality.
  • Community-level hospital and ICU capacity.
  • Short-term forecasting of population incidence and mortality
  • Impacts of spatially targeted interventions on infection risk.
  • Economic and racial inequality in infection and disease risk.

Economic and racial inequity has been a central story of the COVID-19 pandemic

So why were we not prepared for these easy-to-foresee inequities?

Early-pandemic modeling infrastructure lacked theory, intuition, data, and methods for anticipating and targeting inequity

Implications of COVID-19 modeling have been broad, shaping everything from intuition, to public policy and social discource (Figure from (1))

The myth of the equal opportunity infector crowded out inequity as a central concern

What does it mean to be an equal opportunity infector?

  • Susceptibility is uniformly distributed across the population.

  • Host and pathogen biology are the most important factors in determining infectiousness.

  • Protective health behaviors equally available to everyone.

  • Socio-spatial differences in exposure by race and wealth are dwarfed by these biological factors.

We don’t have to believe these things are true for them to end up in our models!

Modeling for equity requires reconceptualizing who our epidemiological models are for.

  • Public health officials making decisions.

  • Scientists and physicians pursuing interventions.

  • Politicians implementing policy and trying to maintain power.

  • Media constructing an easily-digestible narrative.

  • These groups share a top-down view from a position of power and authority.

This led to a focus on a set of outcomes that align with these interests

  • Incidence of disease over time.
  • Prevalence of infection at any given moment in time.
  • Mortality and case-fatality rates.
  • Distributions of infection and mortality by age.
  • Distribution of infection and mortality by neighborhood, socieconomic status, and race/ethnicity.

What can we do?

We now have an opportunity to reflect on the goals of modeling and analysis in public health

COVID-19 disparities are not the fault of those who are experiencing them, but rather reflect social policies and systems that create health disparities in good times and inflate them in a crisis. The US must develop a new kind of “herd immunity,” whereby resistance to the spread of poor health in the population occurs when a sufficiently high proportion of individuals, across all racial, ethnic, and social class groups, are protected from and thus “immune” to negative social determinants.

From Williams & Cooper, “COVID-19 and Health Equity—A New Kind of “Herd Immunity”, JAMA, 6/23/2020

Modeling for equity: A three-legged stool

  1. Picking the right questions.

  2. Collecting and utilizing data that are up to the task of answering the question.

  3. Employing appropriate contrasts and statistical methods.

What should an alternative approach account for?

  • Model spatially localized infection risks as a function of upstream social determinants.
  • Represent correlation between effects of factors such as racism and SES on spatial variation in infection risks.
  • Examine differential impacts of social inequity on contact, susceptibility, and infectiousness.

Zelner et al. (2022), There are no equal opportunity infectors. PLOS Computational Biology

Equity-oriented models require a detailed understanding of the connections between high-level, intermediary, and proximal causes of infection

Flow diagram illustrating connections between high-level, structural causes, intervening mechanisms, and downstream exposure risks from (2)

Equity-oriented models do not treat race as a causal variable

Theoretical diagram from (3) showing the complex relationship between racism, socioeconomic inequity and racial inequity in disease outxomes.

Equity-oriented models account for intersecting identities

Suggestions for how to account for structural racism in epidemiological analyses from (4)

Equity-oriented models use contrasts that reflect structural change

Proportion of SARS-CoV-2 deaths potentially averted in 2020 if all Michiganders experienced the same pacing of SARS-CoV-2 infection as Whites (From Naraharisetti et al., Under)

Equity-oriented models engage political questions

From Nande et al. “The effect of eviction moratoria on the transmission of SARS-CoV-2”, Nature Communications, 2021

Outstanding Challenges

  • Equity remains a subsidiary rather than central concern.

  • Incomplete, NMAR data on race/ethnicity in passive surveillance. (5)

  • Nonexistent data on individual socioeconomic status in most public health surveillance data.

  • Career/funding incentives have not been well-aligned.

  • Modelers from marginalized backgrounds remain underrepresented.


  • Please get in touch with any questions/thoughts/concerns at [email protected].

  • Check out our work at


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Noppert GA, Hegde ST, Kubale JT. Exposure, Susceptibility, and Recovery: A Framework for Examining the Intersection of the Social and Physical Environment and Infectious Disease Risk. American Journal of Epidemiology [electronic article]. 2022;kwac186. ( (Accessed November 3, 2022)
Phelan JC, Link BG. Is Racism a Fundamental Cause of Inequalities in Health? Annual Review of Sociology [electronic article]. 2015;41(1):311–330. ( (Accessed December 15, 2019)
Adkins-Jackson PB, Chantarat T, Bailey ZD, et al. Measuring Structural Racism: A Guide for Epidemiologists and Other Health Researchers. American Journal of Epidemiology [electronic article]. 2021;kwab239. ( (Accessed February 8, 2022)
Trangucci R, Chen Y, Zelner J. Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to non-random missingness. 2022;( (Accessed December 8, 2022)