{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:49:11Z","timestamp":1771508951113,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden\u2019s telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection. Subsequently, an advanced anomaly detection technique is applied incrementally to these time-series symptoms as univariate and multivariate approaches to assess the effectiveness of a machine learning-based method on early detection of the COVID-19 outbreak.<\/jats:p>","DOI":"10.3233\/shti240807","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:50:02Z","timestamp":1724410202000},"source":"Crossref","is-referenced-by-count":3,"title":["Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5191-0424","authenticated-orcid":false,"given":"Atiye Sadat","family":"Hashemi","sequence":"first","affiliation":[{"name":"Center for Applied Intelligent Systems Research, Halmstad University, Sweden"},{"name":"Division of Occupational and Environmental Medicine, Lund University, Sweden"}]},{"given":"Mirfarid Musavian","family":"Ghazani","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research, Halmstad University, Sweden"}]},{"given":"Mattias","family":"Ohlsson","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research, Halmstad University, Sweden"},{"name":"Centre for Environmental and Climate Science, Lund University, Sweden"}]},{"given":"Jonas","family":"Bj\u00f6rk","sequence":"additional","affiliation":[{"name":"Division of Occupational and Environmental Medicine, Lund University, Sweden"},{"name":"Clinical Studies Sweden, Forum South, Sk\u00e5ne University Hospital, Lund, Sweden"}]},{"given":"Dominik","family":"Dietler","sequence":"additional","affiliation":[{"name":"Division of Occupational and Environmental Medicine, Lund University, Sweden"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:50:04Z","timestamp":1724410204000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240807","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}