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Influenza detection and prediction algorithms: comparative accuracy trial in Ostergotland county, Sweden, 2008-2012
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research.
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health.ORCID iD: 0000-0001-6049-5402
2017 (English)In: Epidemiology and Infection, ISSN 0950-2688, E-ISSN 1469-4409, Vol. 145, no 10, 2166-2175 p.Article in journal (Refereed) Published
Abstract [en]

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for nowcasting (integrated detection and prediction) of influenza activity are warranted.

Place, publisher, year, edition, pages
CAMBRIDGE UNIV PRESS , 2017. Vol. 145, no 10, 2166-2175 p.
Keyword [en]
Algorithms; epidemiological methods; evaluation research; human influenza; signal detection analysis
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-139619DOI: 10.1017/S0950268817001005ISI: 000404243900022PubMedID: 28511741OAI: oai:DiVA.org:liu-139619DiVA: diva2:1133735
Note

Funding Agencies|Swedish Civil Contingencies Agency [2010-2788]; Swedish Research Council [2008-5252]

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-08-16

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Spreco, ArminEriksson, OlleDahlström, ÖrjanTimpka, Toomas
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