# Multivariate spatiotemporal models to quantify disparities in COVID-19 health outcomes

> **NIH NIH R21** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2022 · $242,655

## Abstract

PROJECT SUMMARY
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019
(COVID-19), has created a global public health crisis since its onset in late 2019. Although the pandemic has
affected all communities, recent work suggests that socially vulnerable populations have been disproportionately
impacted by the disease. Mounting evidence has found that the pandemic disproportionately affects people of
color, older individuals, and those of lower socioeconomic status. To date, however, there has been no
comprehensive spatiotemporal analysis of the relationship between social vulnerability and COVID-19 outcomes
at a national scale and over an extended period of time, in part because the statistical tools needed for such an
analysis are lacking. The objective of the proposal is to develop multivariate models to identify spatiotemporal
trends in correlated count outcomes, and to use these models to quantify disparities in COVID-19 infection,
death, testing, hospitalizations, and vaccinations across socially vulnerable communities. Aim 1 proposes a
Bayesian multivariate spatiotemporal model to quantify disparities in COVID-19 infection, death, testing,
hospitalization, and vaccination rates over time across US counties. Social vulnerability exposures are
incorporated into the model in a nonlinear and interactive manner through a novel multivariate kernel machine
regression. Aim 2 extends the method to the zero inflated setting by developing a Bayesian multivariate zero-
inflated negative binomial model to quantify disparities in COVID-19 trends over time and across counties. Aim
3 develops computationally scalable Bayesian software for implementation of the methods. The pandemic has
caused enduring disruptions to the health care system that will disproportionately impact vulnerable populations
for years to come. The statistical methods developed here will play a critical role in promoting health equity and
mitigating long-standing disparities exacerbated by the pandemic.

## Key facts

- **NIH application ID:** 10527208
- **Project number:** 1R21MD016947-01A1
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Brian Neelon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,655
- **Award type:** 1
- **Project period:** 2022-09-19 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10527208

## Citation

> US National Institutes of Health, RePORTER application 10527208, Multivariate spatiotemporal models to quantify disparities in COVID-19 health outcomes (1R21MD016947-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10527208. Licensed CC0.

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