# Accelerating Excellence in Translational Science (AXIS)

> **NIH NIH U54** · CHARLES R. DREW UNIVERSITY OF MED & SCI · 2020 · $179,375

## Abstract

Abstract
This project will use national and local incidence, mortality and testing data, with GIS driven
agent-based disease modelling to identify the “neighborhood effects” that drive the uneven
patterns of transmission and serious outcomes from COVID-19. Neighborhood environments
have been associated with chronic disease mortality, disability, cumulative stress, cognitive
decline, loss of physical functioning. These same neighborhood effects are implicit in
exacerbating disparities in the spread and health outcomes of COVID-19, however the exact
mechanisms and the magnitude of the impact of entrenched social disparities specifically on
COVID-19 outcomes are not yet known. The key hypothesis is that agent based disease spread
modeling of the existing retrospective COVID-19 data sources at the national and local levels
with geospatial data input and spatial-temporal analysis will provide powerful knowledge on the
structural factors that influenced the pandemic’s spread in local areas and will facilitate the
development of valuable recommendations on how to mitigate current and future disparities in
impacts of COVID-19 and other future infectious epidemics and pandemics. We will test our key
hypothesis and accomplish our objectives via the following Specific Aims.
Determine what Counties factors related to the social determinants of health have influenced
the spread of coronavirus in the United States.
 1) Determine, within the identified Counties, which social determinants have had significant
 impact on either spread or inhibit spread of CV-19.
 2) Determine which populations groups have been impacted more severely in these local
 contexts and why?
 3) Determine what specific recommendations can be made from these findings?
The expected outcomes of this research are published manuscripts and scholarly presentations
that provide science- based recommendations on the how health authorities and policy makers
can proactively address social factors that amplify disease and poor health outcomes in certain
communities.

## Key facts

- **NIH application ID:** 10212836
- **Project number:** 3U54MD007598-12S6
- **Recipient organization:** CHARLES R. DREW UNIVERSITY OF MED & SCI
- **Principal Investigator:** Jaydutt V. Vadgama
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $179,375
- **Award type:** 3
- **Project period:** 2009-09-28 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212836

## Citation

> US National Institutes of Health, RePORTER application 10212836, Accelerating Excellence in Translational Science (AXIS) (3U54MD007598-12S6). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10212836. Licensed CC0.

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