{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T11:15:07Z","timestamp":1783336507097,"version":"3.54.6"},"reference-count":70,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Management Science"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>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\u2019s 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\u2019s 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\u2019s 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.<\/jats:p>\n                  <jats:p>This paper was accepted by Carri Chan, healthcare management.<\/jats:p>\n                  <jats:p>Funding: This work was supported by the Provost\u2019s 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].<\/jats:p>\n                  <jats:p>Supplemental Material: The online appendices and data files are available at https:\/\/doi.org\/10.1287\/mnsc.2021.04217 .<\/jats:p>","DOI":"10.1287\/mnsc.2021.04217","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:33:51Z","timestamp":1760114031000},"page":"5507-5527","source":"Crossref","is-referenced-by-count":1,"title":["Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors"],"prefix":"10.1287","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8252-1584","authenticated-orcid":false,"given":"Jiayue","family":"Wan","sequence":"first","affiliation":[{"name":"School of Operations Research & Information Engineering, Cornell University, Ithaca, New York 14850"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7991-6385","authenticated-orcid":false,"given":"Yujia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Applied Mathematics, Cornell University, Ithaca, New York 14850"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3501-3341","authenticated-orcid":false,"given":"Peter I.","family":"Frazier","sequence":"additional","affiliation":[{"name":"School of Operations Research & Information Engineering, Cornell University, Ithaca, New York 14850"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2018.3138"},{"key":"B2","doi-asserted-by":"crossref","unstructured":"Augenblick N, Kolstad JT, Obermeyer Z, Wang A (2020) Group testing in a pandemic: The role of frequent testing, correlated risk, and machine learning. 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