Optimal Risk-based Pooled Testing in Public Health Screening, with Equity and Robustness Considerations

Aprahamian, Hrayer Yaznek Berg

Optimal Risk-based Pooled Testing in Public Health Screening, with Equity and Robustness Considerations

dc.contributor.author	Aprahamian, Hrayer Yaznek Berg	en
dc.contributor.committeechair	Bish, Ebru K.	en
dc.contributor.committeechair	Bish, Douglas R.	en
dc.contributor.committeemember	Moran Ramirez, Diego	en
dc.contributor.committeemember	Bansal, Manish	en
dc.contributor.department	Industrial and Systems Engineering	en
dc.date.accessioned	2019-10-26T06:00:37Z	en
dc.date.available	2019-10-26T06:00:37Z	en
dc.date.issued	2018-05-03	en
dc.description.abstract	Group (pooled) testing, i.e., testing multiple subjects simultaneously with a single test, is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease, genetic disorder, or a product defect). While group testing is used in various contexts (e.g., screening donated blood or for sexually transmitted diseases), a lack of understanding of how an optimal grouping scheme should be designed to maximize classification accuracy under a budget constraint hampers screening efforts. We study Dorfman and Array group testing designs under subject-specific risk characteristics, operational constraints, and imperfect tests, considering classification accuracy-, efficiency-, robustness-, and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties provide us with key insights and allow us to model the testing design problems as network flow problems, develop efficient algorithms, and derive insights on equity and robustness versus accuracy trade-off. One of our models reduces to a constrained shortest path problem, for a special case of which we develop a polynomial-time algorithm. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. Our case studies, on chlamydia screening and screening of donated blood, demonstrate the value of optimal risk-based testing designs, which are shown to be less expensive, more accurate, more equitable, and more robust than current screening practices.	en
dc.description.abstractgeneral	Group (pooled) testing, i.e., testing multiple subjects simultaneously with a single test, is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease, genetic disorder, or a product defect). While group testing is used in various contexts (e.g., screening donated blood or for sexually transmitted diseases), a lack of understanding of how an optimal grouping scheme should be designed to maximize classification accuracy under a budget constraint hampers screening efforts. We study Dorfman and Array group testing designs under subject-specific risk characteristics, operational constraints, and imperfect tests, considering classification accuracy-, efficiency-, robustness-, and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties provide us with key insights and allow us to model the testing design problems as network flow problems, develop efficient algorithms, and derive insights on equity and robustness versus accuracy trade-off. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. Our case studies, on chlamydia screening and screening of donated blood, demonstrate the value of optimal risk-based testing designs, which are shown to be less expensive, more accurate, more equitable, and more robust than current screening practices.	en
dc.description.degree	PHD	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:14989	en
dc.identifier.uri	http://hdl.handle.net/10919/95169	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Public Health Screening	en
dc.subject	Group Testing	en
dc.subject	Risk-based Testing	en
dc.subject	Robust Optimization	en
dc.subject	Combinatorial Optimization	en
dc.subject	Dilution effect of Pooling	en
dc.title	Optimal Risk-based Pooled Testing in Public Health Screening, with Equity and Robustness Considerations	en
dc.type	Dissertation	en
thesis.degree.discipline	Industrial and Systems Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	PHD	en