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The objective of the present study was to develop syndromic surveillance monitoring plans from routinely collected ED data with application to detecting disease outbreaks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study involved secondary data analysis of ED presentations to major public hospitals in Queensland and South Australia spanning 2017\u20132020. Monitoring plans were developed for all major Queensland and South Australian public hospitals using an adaptation of Exponentially Weighted Moving Averages \u2013 a process control method used in detecting anomalies in industrial production processes. The methods rely on setting a threshold (control limit) relating to the time between an event of interest (e.g. flu outbreak) using ED presentations as a signal to monitor. An outbreak is flagged as this time gets significantly smaller, and each event offers a decision point on whether an outbreak has occurred. The models incorporate differing levels of temporal memory to cover outbreaks of different sizes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The novel approach to real\u2010time outbreak detection indicates outbreaks for individual hospitals coinciding with the first wave of the COVID\u201019 outbreak in Queensland and South Australia as well as the large 2017 and 2019 influenza seasons.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Outbreak detection models demonstrate the ability to quickly flag an outbreak based on clinician\u2010assigned ED diagnoses. An implemented syndromic surveillance approach can pick up geographic outbreaks quickly so they can be contained. Such capability can help with surveillance related to the current COVID\u201019 pandemic and potential future pandemics.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1742-6723.13907","type":"journal-article","created":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T16:17:32Z","timestamp":1637597852000},"page":"92-98","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Syndromic surveillance to detect disease outbreaks using time between emergency department presentations"],"prefix":"10.1111","volume":"34","author":[{"given":"Justin","family":"Boyle","sequence":"first","affiliation":[{"name":"CSIRO Health and Biosecurity Royal Brisbane and Women's Hospital  Brisbane Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ross","family":"Sparks","sequence":"additional","affiliation":[{"name":"Analytics and Decision Sciences CSIRO Data61  Sydney New South Wales Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268820001314"},{"key":"e_1_2_9_3_1","first-page":"103660","article-title":"An aberration detection\u2010based approach for sentinel syndromic surveillance of COVID\u201019 and other novel Influenza\u2010like illnesses","volume":"113","author":"Wen A","year":"2020","journal-title":"medRxiv"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2196\/18948"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2020.1365"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.abc1126"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2471\/BLT.17.194514"},{"key":"e_1_2_9_8_1","unstructured":"SullivanS PenningtonK RaupachJet al.A Summary of Influenza Surveillance Systems in Australia 2015. 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