{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:26Z","timestamp":1747216166730,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684567"},{"type":"electronic","value":"9781643684574"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>Early detection and prediction of disease outbreaks are crucial for public health service delivery, containment response, saving patient lives, and reducing costs. We propose a new data-driven statistical methodology for outbreak detection and prediction based on routinely collected hospital Emergency Department data. The time between consecutive ED presentations matching a diagnosis of interest forms the basis of a novel index measure to signal that an outbreak has occurred. We validate the method using historical presentations of influenza-like illness made to a large sample of public hospital EDs in 2020 and compare outbreaks identified by the method with the start of the first wave of COVID-19. The method shows promise within the field of disease outbreak detection.<\/jats:p>","DOI":"10.3233\/shti231092","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:32Z","timestamp":1706178332000},"source":"Crossref","is-referenced-by-count":0,"title":["A New Statistical Method to Detect Disease Outbreaks from Hospital Emergency Department Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1772-7911","authenticated-orcid":false,"given":"Jin","family":"Yoon","sequence":"first","affiliation":[{"name":"Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9025-1441","authenticated-orcid":false,"given":"Justin","family":"Boyle","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231092","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:33Z","timestamp":1706178333000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231092","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}