# Real-time syndromic surveillance and modeling to inform decision-making for COVID-19

> **NIH NIH R01** · GEORGETOWN UNIVERSITY · 2020 · $30,885

## Abstract

The rapid spread of COVID-19 around the United States has created an unprecedented
public health emergency. It is now clearly appreciated that smart policy responses to this
pandemic require the utilization of reliable, validated transmission models. Models are
critical both in terms of forecasting the spatio-temporal spread of the virus, but also in
permitting a rational comparison of alternative non-pharmaceutical intervention strategies.
To fill this urgent surveillance gap and inform policy decisions, we propose to model the
spatio-temporal dynamics of COVID-19 in the US from novel streams of real-time
healthcare data. Our combination of sophisticated computational and statistical models,
together with unique high-resolution data will allow a careful characterization of the burden
of COVID-19 beyond tested cases, discriminate among alternative mitigation policies, and
quantify the geographic variation in population immunity as we prepare for the Fall wave.

## Key facts

- **NIH application ID:** 10145858
- **Project number:** 3R01GM123007-03S1
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Shweta Bansal
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $30,885
- **Award type:** 3
- **Project period:** 2017-09-16 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145858

## Citation

> US National Institutes of Health, RePORTER application 10145858, Real-time syndromic surveillance and modeling to inform decision-making for COVID-19 (3R01GM123007-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10145858. Licensed CC0.

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