# Using Road Traffic Data to Identify COVID-19 Priority Testing Locations in Southern California

> **NIH NIH R21** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $165,431

## Abstract

Project Summary
  Without a vaccine or effective treatment, there is an urgent need for performing widespread
  COVID-19 testing to control disease spread. However, complete population testing is
  prohibitively challenging as testing supplies are limited and require trained health staff which
  could be better used in caring for those confirmed to be infected. It is therefore critical to focus
  testing in high-priority areas, where tests are likely to capture positive cases. Identifying infected
  individuals quickly as tests become more widely available will provide crucial information on
  overall disease prevalence to inform future disease control efforts.
  We can help identify areas of potentially high disease prevalence by synthesizing and using
 traffic patterns, as transportation patterns may shed light on possible transmission patterns in
 Los Angeles County (LAC). We propose using the USC Archived Data Management System
 (ADMS), which collects and synthesizes traffic data, to create an epidemic model informed by
 up-to-date origin-destination traffic information. We will use the model to identify which of the 26
 health districts in LAC are at highest risk for unidentified cases and optimally locate testing sites
 within these regions. This allows our recommendations to incorporate change in transportation
 patterns as social distancing recommendations evolve. Specifically, we will partner with the LA
 County Department of Public Health to:
 1. Use road sensor data to analyze traffic patterns in Los Angeles County to understand
 the impact of social distancing guidelines on population flow.
 2. Develop a dynamic transmission network model of COVID-19 using results from Aim 1
 and disease parameters from the medical literature to identify high priority districts for
 testing.
 3. Develop a location model to optimally place drive-through testing sites in these districts.
 The proposed work will use methodology from infectious disease transmission models, traffic
 data, and facility location models together in a novel way. Not only will we provide much needed
 insight using empirical data into population flow dynamics in the context of social distancing
 recommendations, we will shed light on infectious disease modeling more generally. By creating
 a compartmental network model with realistic, time-varying travel patterns in a large
 metropolitan area, the proposed work will further our understanding of the impacts of structural
 modeling assumptions on disease prediction.

## Key facts

- **NIH application ID:** 10472496
- **Project number:** 5R21LM013697-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Sze-Chuan Suen
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $165,431
- **Award type:** 5
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10472496

## Citation

> US National Institutes of Health, RePORTER application 10472496, Using Road Traffic Data to Identify COVID-19 Priority Testing Locations in Southern California (5R21LM013697-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10472496. Licensed CC0.

---

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