# Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $114,457

## Abstract

ABSTRACT
Since early January 2020, our interdisciplinary research team has conducted several studies to elucidate the
emerging threat of COVID-19 and support public health responses throughout the United States, resulting in
peer-reviewed publications, online COVID-19 forecasting tools, and extensive engagement with city, state and
national decision makers. In our collaboration with the CDC to develop a national modeling resource for
pandemic preparedness, we had recently developed a national model for evaluating multi-layered intervention
strategies to contain and mitigate outbreaks in US cities. We adapted the model to COVID-19 by incorporating
the latest estimates for age- and risk-group specific rates of transmission, disease progression, asymptomatic
infections, and severity (including risks of hospitalization, critical care, ventilation and death). The model is
designed to flexibly incorporate combinations of social distancing, contact tracing-isolation, antiviral prophylaxis
and treatment, as well as vaccination strategies.
Our Supplementary Aims propose to build a more granular and data-driven model of COVID-19 to elucidate
the transmission, identify high-risk populations, surveillance targets and effective control of this and future
epidemics within US cities. Aim S1: Focusing initially on the Austin-Round Rock metropolitan area in Texas,
we will apply these models to improve real-time risk assessments and optimize the timing and extent of layered
social distancing measures. Aim S2: We will rapidly evaluate strategies for rolling out antiviral prophylaxis and
therapy based on clinical trial data. Aim S3: We will develop user interfaces for our Austin and national models
to support both scientific research and public health efforts to mitigate COVID-19 and plan for future pandemic
threats. These Aims are synergistic with Specific Aim 2 of our parent grant (R01 AI151176-01), in which we are
developing high-resolution models of viral transmission to improve the early detection and control of
anomalous respiratory viruses, particularly in at risk populations.

## Key facts

- **NIH application ID:** 10399134
- **Project number:** 3R01AI151176-03S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** ALISON P GALVANI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $114,457
- **Award type:** 3
- **Project period:** 2020-07-07 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10399134

## Citation

> US National Institutes of Health, RePORTER application 10399134, Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data (3R01AI151176-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10399134. Licensed CC0.

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