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

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $611,043

## Abstract

Project Abstract/Summary
Our interdisciplinary research team will develop algorithms to accelerate the detection of respiratory virus
outbreaks at an unprecedented local scale in US cities. We propose to advance outbreak detection by
combining machine learning data integration methods and spatial models of disease transmission. The
dynamic models that will be developed will provide mechanistic engines for distinguishing typical from
atypical disease trends and the optimization methods evaluate the informativeness of data sources to
achieve specified public health goals through the rapid evaluation of diverse input data sources. Working
with local healthcare and public health leaders, we will translate the algorithms into user-friendly online tools
to support preparedness plans and decision-making.
Our proposed research is organized around three major aims. In Aim 1, we will apply machine learning and
signal processing methods to build systems that track the earliest indicators of emerging outbreaks within
seven US cities. We will evaluate non-clinical data reflecting early and mild symptoms as well as clinical data
covering underserved communities and geographic and demographic hotspots for viral emergence. In Aim
2, we will develop sub-city scale models reflecting the syndemics of co-circulating respiratory viruses and
chronic respiratory diseases (CRD) that can exacerbate viral infections. We will infer viral transmission rates
and socio-environmental risk cofactors by fitting the model to respiratory disease data extracted from
millions of electronic health records (EHRs) for the last nine years. We will then partner with clinical and
EHR experts to translate our models into the first outbreak detection system for severe respiratory viruses
that incorporates EHR data on CRDs. Using machine learning techniques, we will further integrate other
surveillance, environmental, behavioral and internet predictor data sources to maximize the accuracy,
sensitivity, speed and population coverage of our algorithms. In Aim 3, we will develop an open-access
Python toolkit to facilitate the integration of next generation data into outbreak surveillance models.
This project will produce practical early warning algorithms for detecting emerging viral threats at high
spatiotemporal resolution in several US cities, elucidate socio-geographic gaps in current surveillance
systems and hotspots for viral emergence, and provide a robust design framework for extrapolating these
algorithms to other US cities.

## Key facts

- **NIH application ID:** 9946212
- **Project number:** 1R01AI151176-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** ALISON P GALVANI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $611,043
- **Award type:** 1
- **Project period:** 2020-02-24 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9946212

## Citation

> US National Institutes of Health, RePORTER application 9946212, Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data (1R01AI151176-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9946212. Licensed CC0.

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