Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19

dc.contributor.authorEl Hajj, Hussein
dc.contributor.authorBish, Douglas R.
dc.contributor.authorBish, Ebru K.
dc.contributor.authorAprahamian, Hrayer
dc.contributor.otherVirginia Polytechnic Institute & State University
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherTexas A&M University College Station
dc.date.accessioned2023-09-28T19:38:33Z
dc.date.available2023-09-28T19:38:33Z
dc.date.issued2022
dc.description.abstractTesting provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationEl Hajj, H., Bish, D. R., Bish, E. K., & Aprahamian, H. (2021). Screening multi‐dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to <scp>COVID</scp>‐19. In Naval Research Logistics (NRL) (Vol. 69, Issue 1, pp. 3–20). Wiley. https://doi.org/10.1002/nav.21985
dc.identifier.doi10.1002/nav.21985
dc.identifier.orcidhttps://orcid.org/0000-0002-1376-8268
dc.identifier.orcidhttps://orcid.org/0000-0002-2653-7135
dc.identifier.orcidhttps://orcid.org/0000-0002-8364-4586
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11659
dc.languageEnglish
dc.language.isoen_US
dc.publisherWiley
dc.subjectCOVID-19 testing
dc.subjectheterogeneous population
dc.subjectpartition problem
dc.subjectpooled testing
dc.subjectresource allocation under limited resources
dc.subjectOPTIMAL INVENTORY GROUPINGS
dc.subjectPARTITIONING PROBLEM
dc.subjectRISK
dc.subjectOperations Research & Management Science
dc.titleScreening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources, with application to COVID-19en_US
dc.typeArticle
dc.typetext
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