# Assessing the impact of COVID-19 interventions on human mobility and SARS-CoV-2 transmission dynamics in the United States

> **NIH NIH R21** · UNIVERSITY OF NOTRE DAME · 2022 · $195,625

## Abstract

Project Summary
The rapid spread of SARS-CoV-2 led countries across the globe to implement strong social distancing and
lockdown measures to reduce transmission. The first peak of newly reported COVID-19 cases and deaths in
the United States occurred in April 2020, but the number of positive tests began increasing again in June as
many states started easing their initial shelter-in-place orders despite ongoing transmission. In the absence of
widespread deployment of an effective vaccine or another pharmaceutical intervention, state and local
governments will have to rely on a range of non-pharmaceutical interventions (NPIs) to limit further outbreaks
over the next 12-24 months. To provide policy makers with actionable information regarding the efficacy of
different NPIs under a range of realistic epidemiological contexts, we will examine the impact of different NPIs
on both mobility patterns and disease transmission using a geographically realistic, agent-based model. First,
we will assemble a comprehensive database of local, county, and state policies related to COVID-19 from
public websites and social media and categorize these policies by intervention type. We will also obtain
epidemiological data from several different publicly available databases and use county-level case, testing,
hospitalization, and mortality data to assess the impact of different county and state policies and NPIs in real-
time.
We will assess the link between NPIs and SARS-CoV-2 dynamics using cell phone-derived mobility data from
a combination of publicly available sources and data sharing agreements with several data providers. First, we
will use statistical models to assess the impact of different categories of county and state COVID-19 policies
and NPIs on epidemiologically-relevant human mobility and activity patterns, including activity data at different
places-of-interest subject to particular COVID-19 related restrictions. These mobility metrics will then be used
to inform changes in local contact patterns in our agent-based transmission model. This transmission model
will also incorporate detailed information on the demographics, socioeconomic factors, co-morbidities, and
occupations that have been shown to be important for SARS-CoV-2 epidemiology. Local populations will be
linked using regional connectivity metrics derived from cell phone data. Incorporation of these details will allow
us to estimate the impact of different policies on transmission dynamics in a range of settings while accounting
for local conditions as well as regional dynamics. Model estimates will be iteratively updated on a weekly basis
over the course of the project to provide short-term forecasts of infections, hospitalizations, and deaths based
on the current mix of NPIs across the country. These forecasts will be used to validate our NPI-impact
estimates by comparing forecasts to future observations.

## Key facts

- **NIH application ID:** 10434915
- **Project number:** 5R21AI164391-02
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Sean Michael Moore
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $195,625
- **Award type:** 5
- **Project period:** 2021-06-18 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10434915

## Citation

> US National Institutes of Health, RePORTER application 10434915, Assessing the impact of COVID-19 interventions on human mobility and SARS-CoV-2 transmission dynamics in the United States (5R21AI164391-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10434915. Licensed CC0.

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