# Hotspots_COVID Supplement II

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2022 · $323,000

## Abstract

Summary
This application is in response to the urgent need to understand the epidemiological impact of
non-uniform uptake of the newly approved SARS-CoV-2 vaccine for children in the US. Despite
the continuing infections and hospitalizations due to COVID-19, there is no doubt that vaccination
has led to significantly improved outcomes. With the recent approval of vaccinations for young
children, we now have a real opportunity for controlling COVID-19. However, several factors could
pose roadblocks towards this goal: opposition to the vaccine and other policies such as masking,
occupancy limits and testing (especially in schools), new variants and waning immunity. As a result,
the risk for even well vaccinated subgroups, such as seniors, could increase in the coming months.
Modeling this increased risk, and finding the most vulnerable subgroups is a very challenging
problem with high public health impact.
 Our project will develop a rigorous data and model driven approach to identify and characterize
the most vulnerable subgroups, and study strategies for mitigating their risk; this approach will be
evaluated for Virginia. Team members have been supporting the Virginia Dept of Health’s (VDH)
COVID-19 response since the start of the pandemic, and have novel high resolution datasets and
domain expertise through this collaboration.
 We will extend a detailed, age-stratified agent based model (ABM) for SARS-CoV-2 transmis-
sion in Virginia by incorporating detailed datasets on vaccine hesitancy, immunization, and school
mixing and interventions. The calibration, simulation and analysis of such an ABM is difficult in
itself due to its large scale. Using such a model to identify the most vulnerable populations in such
large models is computationally very challenging, and has not been done before. An innovative as-
pect of the project is the combination of tools from AI and high performance computing with large
scale ABMs for such problems. The project will build significantly on tools and models developed
by team members as part of the parent grant.

## Key facts

- **NIH application ID:** 10541335
- **Project number:** 3R01GM109718-09S1
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Achla Marathe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $323,000
- **Award type:** 3
- **Project period:** 2014-08-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10541335

## Citation

> US National Institutes of Health, RePORTER application 10541335, Hotspots_COVID Supplement II (3R01GM109718-09S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10541335. Licensed CC0.

---

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