Culverhouse Researchers Create Improved Means for Medical Testing and Screening
- April 26th, 2021
Limited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the pool tests positive, then a new sample from the original specimen collected from each subject in the pool is individually tested, and if the pool tests negative, each subject in the pool is classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. In research published with Hussein El Hajj of Virginia Tech and Hrayer Aprahamian of Texas A&M, Culverhouse College’s Doug Bish and Ebru Bish develop a mathematical model to determine the best pool size for different risk groups, based on each group’s estimated COVID-19 prevalence.
Their approach considers the sensitivity (the ability of a test to correctly identify those with the disease) and specificity (the ability of a test to correctly identify those with the disease) of the test as well as a dynamic and uncertain prevalence, and provides a robust pool size for each group. The research described in “A Robust Pooled Testing Approach to Expand COVID-19 Screening Capacity,” published by PLoS One in February, results in much shorter times, on average, to get the test results compared to individual testing and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening.
In “Optimal Genetic Screening for Cystic Fibrosis,” El-Hajj, Bish, and Bish develop a decision support model to select which genetic variants to screen for by considering the trade-off between classification accuracy and testing cost as well as the technological constraints that limit the number of variants selected. In this research paper, recently accepted for publication by Operations Research, the authors focus on screening newborns for life-threatening genetic diseases – specifically cystic fibrosis. As part of the cystic fibrosis screening process, all states in the United States use multiple tests, including genetic tests that detect a subset of the over 300 genetic variants (specific mutations) that cause cystic fibrosis. Because variant prevalence rates are highly uncertain, a robust optimization framework is developed. Further, two commonly used cystic fibrosis screening processes are analytically compared, and conditions under which each process dominates are established. A case study based on published data is provided.