{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:08:01Z","timestamp":1788739681332,"version":"build-2803163510"},"reference-count":95,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Siemens Healthcare Diagnostics, Inc","award":["8317147-01"],"award-info":[{"award-number":["8317147-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Early during emerging infectious disease outbreaks, case-based surveillance is constrained by limited test availability, diagnostic delays, and low clinical suspicion. Novel pathogens that mimic symptoms of established conditions may generate detectable outbreak signals in routine testing data, as infected individuals seek testing for known conditions and test negative. We developed analytic and simulation frameworks using Poisson and negative binomial models to evaluate whether total and negative testing volumes for a clinically similar (\u201cmimicking\u201d) condition can provide timely outbreak warning. We systematically assessed detection performance across variations in baseline test counts, epidemic growth rates, testing fractions among cases, detection threshold stringency, and overdispersion. Thresholds based on negative tests consistently outperformed those based on total tests, achieving earlier detection with about one-third fewer cumulative cases while maintaining comparable false-positive rates. However, reliable detection required stringent conditions: high testing fractions among epidemic cases, low baseline test volumes, and low overdispersion. When testing fractions fell below 5%, epidemic size estimates offered little practical value; when above 30%, precision improved substantially. These findings support prioritizing access to disaggregated test result data but caution that this detection approach is best positioned as a resource-efficient complement within integrated surveillance portfolios rather than a standalone early warning system.<\/jats:p>","DOI":"10.1093\/aje\/kwag181","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T06:50:05Z","timestamp":1786431005000},"source":"Crossref","is-referenced-by-count":0,"title":["Leveraging limited testing data for early detection of emerging infectious disease outbreaks"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5306-4523","authenticated-orcid":false,"given":"Alexander John","family":"Zapf","sequence":"first","affiliation":[{"name":"Center for Communicable Disease Dynamics , Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1504-9213","authenticated-orcid":false,"given":"Marc","family":"Lipsitch","sequence":"additional","affiliation":[{"name":"Center for Communicable Disease Dynamics , Department of Epidemiology, Harvard T.H. 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