# Georgia Clinical & Translational Science Alliance (GaCTSA)

> **NIH NIH UL1** · EMORY UNIVERSITY · 2020 · $225,579

## Abstract

EFFECTIVE ALLOCATION OF TEST CENTERS FOR COVID-19 USING MACHINE LEARNING AND
 ADAPTIVE SAMPLING
ABSTRACT
A critical task in managing and dealing with COVID-19 in communities is to perform diagnostic and/or antibody tests to
identify diseased individuals. This information is critical to public health officials to estimate prevalence and transmission,
and to effectively plan for required resources such as ICU beds, ventilators, personal protective equipment, and medical
staff. Additionally, information on the number of infected people can be used to develop probabilistic and statistical models
to estimate the reproduction number of the disease, and to predict the likely spatial and temporal trajectories of the outbreak.
This provides vital information for planning actions and preparing policies and guidelines for social-distancing, school
closures, remote work, community lockdown, etc. Despite the importance of diagnostic testing and identification of the
positive cases, broad-scale testing is a challenging task particularly due to the limited number of test kits and resources. Our
proposed research focuses on the development machine learning-based allocation strategies for determining the optimal
location of COVID-19 test centers, including mobile and satellite centers, to minimize the local and global prediction
uncertainties, maximize geographic coverage, associated with projections of spatio-temporal outbreak trajectories, and to
improve efficient identification of diseased cases.

## Key facts

- **NIH application ID:** 10158891
- **Project number:** 3UL1TR002378-04S2
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Andres J Garcia
- **Activity code:** UL1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $225,579
- **Award type:** 3
- **Project period:** 2017-09-22 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158891

## Citation

> US National Institutes of Health, RePORTER application 10158891, Georgia Clinical & Translational Science Alliance (GaCTSA) (3UL1TR002378-04S2). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10158891. Licensed CC0.

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