 Optimal pooled testing design for prevalence estimation under resource constraintsNgoc T. Nguyen, Ebru K. Bish and Douglas R. Bish Omega, 2021, vol. 105, issue C Abstract: Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via a single test. The pool design, i.e., the number and sizes of testing pools, has a substantial impact on estimation accuracy. Determining an optimal pool design is challenging, especially for emerging or seasonal diseases for which information on the status of the disease is unreliable or unavailable prior to testing. We develop novel optimization models for testing pool design under uncertainty and limited resources, and characterize structural properties of optimal pool designs. We apply our models to estimate the prevalence of West Nile virus in mosquitoes (the main vector of transmission to humans). Our findings suggest that estimation accuracy can be substantially improved over the status quo through the proposed optimal pool designs. Keywords: Optimal testing pool design; Uncertainty; Robust optimization; Prevalence estimation; Emerging or seasonal diseases (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:105:y:2021:i:c:s0305048321001134 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2021.102504 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.