# Identifying Vulnerable Communities for Infectious Disease Outbreaks

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2022 · $49,252

## Abstract

PROJECT SUMMARY
The COVID-19 pandemic’s unequal toll on racial and ethnic minority groups in the United States underscored
that vulnerable communities need unique attention from public health officials to address health disparities
stemming from a cumulative history of injustices. Compared to white Americans, Black and Hispanic Americans
as well as indigenous populations have increased odds of hospitalization and higher deaths rates due to COVID-
19. A rapid, focused public health response is necessary for future outbreak preparedness, especially among
minority populations that are more vulnerable to disease. Artificial Intelligence (AI) has been used to predict
potential disease outbreaks; however, machine learning (ML), a branch of AI, has yet to be broadly used in
identifying vulnerable populations and underserved communities at risk for disease outbreaks and track
heterogeneities in risks at the neighborhood level. Furthermore, while disease incidence is often calculated at a
county or zip code level, understanding heterogeneities in risk among neighborhoods in community transmission
of diseases requires a more granular geographic unit for analysis. To this end, epidemiologic, geospatial, and
machine learning tools to rapidly and accurately identify vulnerable neighborhoods based on local needs will be
imperative to achieve health equity during infectious disease outbreaks. In Aim 1, we will explore associations
and trends between respiratory infectious disease incidence (ex. influenza, tuberculosis, pertussis, and COVID-
19), vaccination coverage (MMR, DTaP, HPV, and influenza), and socioeconomic disadvantage considering
geography in Philadelphia. Area Deprivation Index and Social Vulnerability Index will be used to measure
socioeconomic disadvantage. Poisson and linear regression models will be used to find associations between
infectious disease incidence, low vaccination coverage, and social determinants of health. Bayesian spatial
regression modeling will be used to assess the change in the proportion of vulnerable communities affected by
infectious diseases and identify any gaps in vaccination coverage differentially by neighborhood-level factors. In
Aim 2, we will train a geographic information system (GIS)-based ML model, fit to the aggregated geospatial
disease, vaccination, and social determinants of health data from Aim 1, and test its predictive capability on
Philadelphia COVID-19 case data. Our goal will be to assess the predictive capability of GIS-based ML models
on identifying areas for public health intervention. This innovative research will help us predict neighborhoods at
risk of future infectious disease outbreaks and aid in timely identification of vulnerable populations to guide public
health resources, which would be very useful for emergency preparedness efforts for future infectious disease
outbreaks. The accompanying training plan consists of both didactic and experiential learning opportunities, and
will enable the...

## Key facts

- **NIH application ID:** 10464066
- **Project number:** 1F31MD016796-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Tuhina Srivastava
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $49,252
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10464066

## Citation

> US National Institutes of Health, RePORTER application 10464066, Identifying Vulnerable Communities for Infectious Disease Outbreaks (1F31MD016796-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10464066. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*