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A robust pooled testing approach to expand COVID-19 screening capacity

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  • Douglas R Bish
  • Ebru K Bish
  • Hussein El-Hajj
  • Hrayer Aprahamian

Abstract

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 new samples from the collected specimens are individually tested, while if the pool tests negative, the subjects are classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. We develop a mathematical model to determine the best pool size for different risk groups, based on each group’s estimated COVID-19 prevalence. Our approach takes into consideration the sensitivity and specificity of the test, and a dynamic and uncertain prevalence, and provides a robust pool size for each group. For practical relevance, we also develop a companion COVID-19 pooling design tool (through a spread sheet). To demonstrate the potential value of pooling, we study COVID-19 screening using testing data from Iceland for the period, February-28-2020 to June-14-2020, for subjects stratified into high- and low-risk groups. We implement the robust pooling strategy within a sequential framework, which updates pool sizes each week, for each risk group, based on prior week’s testing data. Robust pooling reduces the number of tests, over individual testing, by 88.5% to 90.2%, and 54.2% to 61.9%, respectively, for the low-risk and high-risk groups (based on test sensitivity values in the range [0.71, 0.98] as reported in the literature). This results in much shorter times, on average, to get the test results compared to individual testing (due to the higher testing throughput), and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening.

Suggested Citation

  • Douglas R Bish & Ebru K Bish & Hussein El-Hajj & Hrayer Aprahamian, 2021. "A robust pooled testing approach to expand COVID-19 screening capacity," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-15, February.
  • Handle: RePEc:plo:pone00:0246285
    DOI: 10.1371/journal.pone.0246285
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    Cited by:

    1. Michela Baccini & Emilia Rocco & Irene Paganini & Alessandra Mattei & Cristina Sani & Giulia Vannucci & Simonetta Bisanzi & Elena Burroni & Marco Peluso & Armelle Munnia & Filippo Cellai & Giampaolo P, 2021. "Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-15, May.
    2. Ramy Elitzur & Dmitry Krass & Eyal Zimlichman, 2023. "Machine learning for optimal test admission in the presence of resource constraints," Health Care Management Science, Springer, vol. 26(2), pages 279-300, June.

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