Academic Journal
Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors.
| Τίτλος: | Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors. |
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| Συγγραφείς: | Wan, Jiayue1 (AUTHOR) jw2529@cornell.edu, Zhang, Yujia2 (AUTHOR) yz685@cornell.edu, Frazier, Peter I.1 (AUTHOR) pf98@cornell.edu |
| Πηγή: | Management Science (INFORMS). Jul2026, Vol. 72 Issue 7, p5507-5527. 21p. |
| Θεματικοί όροι: | *Statistical correlation, *Test methods, Dilution, Medical screening, Sensitivity analysis, Viral load |
| Περίληψη: | Population-wide screening is a powerful tool for controlling infectious diseases. Group testing can enable such screening despite limited resources. Viral concentration of pooled samples are often positively correlated, either because prevalence and sample collection are influenced by location, or through intentional enhancement via pooling samples according to risk or household. Such correlation is known to improve efficiency when test sensitivity is fixed. However, in reality, a test's sensitivity depends on the concentration of the analyte (e.g., viral RNA), as in the so-called dilution effect, where sensitivity decreases for larger pools. We show that concentration-dependent test error alters correlation's effect under the most widely used group testing procedure, the two-stage Dorfman procedure. We prove that when test sensitivity increases with concentration: pooling correlated samples together (correlated pooling) achieves asymptotically higher sensitivity than independently pooling the samples (naive pooling). In contrast, in the concentration-independent case, correlation does not affect sensitivity. Moreover, with concentration-dependent errors, correlation can degrade test efficiency compared with naive pooling, whereas under concentration-independent errors, correlation always improves efficiency. We propose an alternative measure of test resource usage, the number of positives found per test consumed, which we argue is better aligned with infection control, and show that correlated pooling outperforms naive pooling on this measure. In simulation, we show that the effect of correlation under realistic concentration-dependent test error is meaningfully different from correlation's effect assuming fixed sensitivity. Our findings underscore the importance for policy makers of using models that incorporate naturally occurring correlation and of considering ways of strengthening this correlation. This paper was accepted by Carri Chan, healthcare management. Funding: This work was supported by the Provost's Office of Cornell University, the Air Force Office of Scientific Research [Grant FA9550-19-1-0283], and the National Science Foundation Division of Mathematical Sciences [Grant DMS2230023]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04217. [ABSTRACT FROM AUTHOR] |
| Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wan%2C+Jiayue%22">Wan, Jiayue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jw2529@cornell.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yujia%22">Zhang, Yujia</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yz685@cornell.edu</i><br /><searchLink fieldCode="AR" term="%22Frazier%2C+Peter+I%2E%22">Frazier, Peter I.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pf98@cornell.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Management+Science+%28INFORMS%29%22">Management Science (INFORMS)</searchLink>. Jul2026, Vol. 72 Issue 7, p5507-5527. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br />*<searchLink fieldCode="DE" term="%22Test+methods%22">Test methods</searchLink><br /><searchLink fieldCode="DE" term="%22Dilution%22">Dilution</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+screening%22">Medical screening</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Viral+load%22">Viral load</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Population-wide screening is a powerful tool for controlling infectious diseases. Group testing can enable such screening despite limited resources. Viral concentration of pooled samples are often positively correlated, either because prevalence and sample collection are influenced by location, or through intentional enhancement via pooling samples according to risk or household. Such correlation is known to improve efficiency when test sensitivity is fixed. However, in reality, a test's sensitivity depends on the concentration of the analyte (e.g., viral RNA), as in the so-called dilution effect, where sensitivity decreases for larger pools. We show that concentration-dependent test error alters correlation's effect under the most widely used group testing procedure, the two-stage Dorfman procedure. We prove that when test sensitivity increases with concentration: pooling correlated samples together (correlated pooling) achieves asymptotically higher sensitivity than independently pooling the samples (naive pooling). In contrast, in the concentration-independent case, correlation does not affect sensitivity. Moreover, with concentration-dependent errors, correlation can degrade test efficiency compared with naive pooling, whereas under concentration-independent errors, correlation always improves efficiency. We propose an alternative measure of test resource usage, the number of positives found per test consumed, which we argue is better aligned with infection control, and show that correlated pooling outperforms naive pooling on this measure. In simulation, we show that the effect of correlation under realistic concentration-dependent test error is meaningfully different from correlation's effect assuming fixed sensitivity. Our findings underscore the importance for policy makers of using models that incorporate naturally occurring correlation and of considering ways of strengthening this correlation. This paper was accepted by Carri Chan, healthcare management. Funding: This work was supported by the Provost's Office of Cornell University, the Air Force Office of Scientific Research [Grant FA9550-19-1-0283], and the National Science Foundation Division of Mathematical Sciences [Grant DMS2230023]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04217. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/mnsc.2021.04217 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 5507 Subjects: – SubjectFull: Statistical correlation Type: general – SubjectFull: Test methods Type: general – SubjectFull: Dilution Type: general – SubjectFull: Medical screening Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Viral load Type: general Titles: – TitleFull: Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wan, Jiayue – PersonEntity: Name: NameFull: Zhang, Yujia – PersonEntity: Name: NameFull: Frazier, Peter I. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00251909 Numbering: – Type: volume Value: 72 – Type: issue Value: 7 Titles: – TitleFull: Management Science (INFORMS) Type: main |
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