{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:05:41Z","timestamp":1787162741179,"version":"build-2736575974"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"Australian Consortium of Epidemic Forecasting and Analytics (ACEFA), a National Health and Medical Research Council of Australia Centre of Research Excellence","award":["2035303"],"award-info":[{"award-number":["2035303"]}]},{"name":"Australian Research Council Discovery Project Grant","award":["DP240102286"],"award-info":[{"award-number":["DP240102286"]}]},{"name":"Australian Research Council through the Laureate Fellowship Scheme","award":["FL240100126"],"award-info":[{"award-number":["FL240100126"]}]},{"name":"National Health and Medical Research Council of Australia through the Investigator Grant Scheme","award":["2021\/GNT2010051"],"award-info":[{"award-number":["2021\/GNT2010051"]}]},{"name":"University of Melbourne McKenzie fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. Surveillance indicators routinely collected to monitor these trends are often a composite of multiple pathogens. For example, \u201cinfluenza-like illness\u201d\u2014routinely monitored as a proxy for influenza infections\u2014is a symptom definition that could be caused by a wide range of pathogens, including multiple subtypes of influenza, SARS-CoV-2, and RSV. Inferred trends from such composite time series may not reflect the trends of any one of the component pathogens, each of which can exhibit distinct dynamics. Although many surveillance systems routinely test a subset of individuals contributing to a surveillance indicator\u2014providing information on the relative contribution of the component pathogens\u2014trends may be obscured by time-varying testing rates or substantial noise in the observation process. Here we develop a general statistical framework for inferring temporal trends of multiple pathogens from routinely collected surveillance data. We demonstrate its application to three different surveillance systems covering multiple pathogens (influenza, SARS-CoV-2, dengue), locations (Australia, Singapore, USA, Taiwan, UK), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily). This methodology is applicable to a wide range of pathogens and surveillance systems.<\/jats:p>","DOI":"10.1093\/aje\/kwaf119","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T07:49:41Z","timestamp":1749023381000},"page":"2489-2498","source":"Crossref","is-referenced-by-count":2,"title":["Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data"],"prefix":"10.1093","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8086-4495","authenticated-orcid":false,"given":"Oliver","family":"Eales","sequence":"first","affiliation":[{"name":"Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Parkville ,","place":["Australia"]},{"name":"School of Mathematics and Statistics, The University of Melbourne , Parkville ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-8353","authenticated-orcid":false,"given":"Saras M","family":"Windecker","sequence":"additional","affiliation":[{"name":"Infectious Disease Ecology and Modelling, The Kids Research Institute , Perth ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2452-3098","authenticated-orcid":false,"given":"James M","family":"McCaw","sequence":"additional","affiliation":[{"name":"Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Parkville ,","place":["Australia"]},{"name":"School of Mathematics and Statistics, The University of Melbourne , Parkville 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