# Economic security and health disparity in COVID-19: A computational modeling approach.

> **NIH NIH R21** · DUKE UNIVERSITY · 2022 · $131,583

## Abstract

Project summary
Job insecurity and disease risk are inextricably linked, and the SARS-CoV-2 pandemic has highlighted the
interdependence between these two critical outcomes. On the disease transmission side, current models for
disease transmission rest on variants of mass-action Susceptible – (Exposed) – Infected – Removed (SIR,
SEIR) frameworks or curve-fitting models tuned to SIR dynamics. The best of these models use compartments
that are deemed biologically relevant, such as age, but they typically do not include social relevance, effectively
ignoring well-known segregation and social stratification barriers to interaction that likely channel infection. We
urgently need models that accurately account for core population differences in risk and burden of disease.
Disease exposure is deeply structured by the racial and ethnic segregation of communities, differences in living
arrangements, and ability to avoid close personal contact with others, which are compounded by well-known
health disparities and lead to poorer COVID-19 outcomes. By assuming away such features, we miss how
unevenly the burden of disease and disease avoidance activity is shared across vulnerable populations. On the
economic burden side, it is well-known that job insecurity is patterned by race and socioeconomic status in the
United States. African Americans and Latinos are considerably more likely than whites to work in hourly-wage,
precarious jobs, and as a result, these populations are particularly vulnerable to job loss, reductions in income
and benefits, and other job-related cutbacks during economic retrenchments. Similarly, there are marked
gradients along the wealth distribution in economic vulnerability resulting from deficits in savings needed to
cover basic living expenses during periods of income reduction or loss. Importantly, the very same populations
who are economically vulnerable are also at higher risk of contracting diseases like COVID-19. African
Americans, Latinos, and other low-SES populations are at particularly high risk of becoming ill, being
hospitalized, and dying of complications resulting from COVID-19. Importantly, behaviors resulting from job
insecurity are likely to exacerbate disease risk; and disease is likely to exacerbate job insecurity. Most attempts
to model these processes do not take this essential interdependence into account. We propose to build and
test a fully integrated Agent Based Model (ABM) of disease spread and socio-economic outcomes. In Aim 1,
we will build the ABM based on real social network and activity data that reflect the mix of strong ties, weak
ties, and incidental personal contacts. In Aim 2, we fit the ABMs to observed epidemic patterns to identify key
disparity-driving features. In Aim 3, we propose policy alternatives that can help identify inherent tradeoffs
between public safety and economic hardship and how such outcomes are unequally distributed across
working people in the country.

## Key facts

- **NIH application ID:** 10490325
- **Project number:** 5R21HD104431-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** James Moody
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $131,583
- **Award type:** 5
- **Project period:** 2021-09-17 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10490325

## Citation

> US National Institutes of Health, RePORTER application 10490325, Economic security and health disparity in COVID-19: A computational modeling approach. (5R21HD104431-02). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10490325. Licensed CC0.

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