Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71069

Minimizing the usage of SARS-CoV-2 lab test resources through test pooling enhanced by classification techniques

File Size Format  
0368.pdf 454.14 kB Adobe PDF View/Open

Item Summary

Title:Minimizing the usage of SARS-CoV-2 lab test resources through test pooling enhanced by classification techniques
Authors:Garcia, Ana Cristina
Barros, Marcio De Oliveira
Keywords:Optimization, Simulation and IT for Healthcare Processes and Services
intelligent classifier
machine learning
pooling
resource optimization
Date Issued:05 Jan 2021
Abstract:Testing is an effective practice to limit the spread of the SARS-CoV-2. PCR is an accurate method to detect SARS-CoV-2 infected individuals, but PCR lab test kits are scarce and expensive resources. Therefore, their usage should be optimized. Testing in batch (pooling) is a procedure that merges individuals’ swabs, allowing group diagnosis without affecting the accuracy of the results. Savings on test kits depend on the prevalence of the disease, pool composition, and size. We propose a novel approach for optimizing pooling to minimize the usage of lab test kits. We show that estimating the probability of an individual being infected by means of a binary classifier leads to improvements in the efficiency of pooling strategies. We use simulation to select the components of a new pooling strategy based on a classifier and evaluate our approach using a real dataset.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71069
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.453
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Optimization, Simulation and IT for Healthcare Processes and Services


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons