{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:35:12Z","timestamp":1779104112976,"version":"3.51.4"},"reference-count":25,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Epidemic surveillance is a challenging task, especially when crucial data is fragmented across institutions and data custodians are unable or unwilling to share it. This study aims to explore the feasibility of a simple <jats:italic>federated surveillance<\/jats:italic> approach. We conduct hypothesis tests on count data behind each custodian\u2019s firewall and then combine <jats:italic>p<\/jats:italic>-values from these tests using techniques from meta-analysis. We propose a hypothesis testing framework to identify surges in epidemic-related data streams and conduct experiments on real and semi-synthetic data to assess the power of different <jats:italic>p<\/jats:italic>-value combination methods to detect surges without needing to combine or share the underlying counts. Our findings show that relatively simple combination methods achieve a high degree of fidelity and suggest that infectious disease outbreaks can be detected without needing to share or even aggregate data across institutions.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012907","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T18:19:17Z","timestamp":1744136357000},"page":"e1012907","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Federated epidemic surveillance"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7062-1476","authenticated-orcid":true,"given":"Ruiqi","family":"Lyu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roni","family":"Rosenfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Wilder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"issue":"51","key":"pcbi.1012907.ref001","doi-asserted-by":"crossref","first-page":"e2111452118","DOI":"10.1073\/pnas.2111452118","article-title":"An open repository of real-time COVID-19 indicators","volume":"118","author":"A Reinhart","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"51","key":"pcbi.1012907.ref002","doi-asserted-by":"crossref","first-page":"e2111453118","DOI":"10.1073\/pnas.2111453118","article-title":"Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?","volume":"118","author":"DJ McDonald","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"51","key":"pcbi.1012907.ref003","doi-asserted-by":"crossref","first-page":"e2111454118","DOI":"10.1073\/pnas.2111454118","article-title":"The US COVID-19 trends and impact survey: continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination","volume":"118","author":"JA Salomon","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1012907.ref004","volume-title":"Testing statistical hypotheses","author":"JP Romano","year":"2005"},{"key":"pcbi.1012907.ref005","unstructured":"Fay MP. 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