A proactive/reactive mass screening approach with uncertain symptomatic cases DOI Creative Commons
Jiayi Lin, Hrayer Aprahamian, George Golovko

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(8), P. e1012308 - e1012308

Published: Aug. 14, 2024

We study the problem of mass screening heterogeneous populations under limited testing budget. Mass is an essential tool that arises in various settings, e.g., COVID-19 pandemic. The objective to classify entire population as positive or negative for a disease efficiently and accurately possible. Under budget, facilities need allocate portion budget target sub-populations (i.e., proactive screening) while reserving remaining screen symptomatic cases reactive screening). This paper addresses this decision by taking advantage accessible population-level risk information identify optimal set proactive/reactive screening. framework also incorporates two widely used schemes: Individual Dorfman group testing. By leveraging special structure resulting bilinear optimization problem, we key structural properties, which turn enable us develop efficient solution schemes. Furthermore, extend model accommodate customized schemes across different introduce highly heuristic algorithm generalized model. conduct comprehensive case on US, utilizing geographically-based data. Numerical results demonstrate significant improvement up 52% total misclassifications compared conventional strategies. In addition, our offers valuable managerial insights regarding allocation measures diverse geographic regions.

Language: Английский

A proactive/reactive mass screening approach with uncertain symptomatic cases DOI Creative Commons
Jiayi Lin, Hrayer Aprahamian, George Golovko

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(8), P. e1012308 - e1012308

Published: Aug. 14, 2024

We study the problem of mass screening heterogeneous populations under limited testing budget. Mass is an essential tool that arises in various settings, e.g., COVID-19 pandemic. The objective to classify entire population as positive or negative for a disease efficiently and accurately possible. Under budget, facilities need allocate portion budget target sub-populations (i.e., proactive screening) while reserving remaining screen symptomatic cases reactive screening). This paper addresses this decision by taking advantage accessible population-level risk information identify optimal set proactive/reactive screening. framework also incorporates two widely used schemes: Individual Dorfman group testing. By leveraging special structure resulting bilinear optimization problem, we key structural properties, which turn enable us develop efficient solution schemes. Furthermore, extend model accommodate customized schemes across different introduce highly heuristic algorithm generalized model. conduct comprehensive case on US, utilizing geographically-based data. Numerical results demonstrate significant improvement up 52% total misclassifications compared conventional strategies. In addition, our offers valuable managerial insights regarding allocation measures diverse geographic regions.

Language: Английский

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