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Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design
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.ORCID iD: 0000-0002-1551-1722
University of Hong Kong, Peoples R China.
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2017 (English)In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 19, no 6, article id e211Article in journal (Refereed) Published
Abstract [en]

Background: Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective: The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods: An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results: The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions: Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified.

Place, publisher, year, edition, pages
JMIR PUBLICATIONS, INC , 2017. Vol. 19, no 6, article id e211
Keywords [en]
human influenza; algorithms; epidemiological surveillance; public health surveillance; evaluation research; epidemiological methods
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:liu:diva-140808DOI: 10.2196/jmir.7101ISI: 000408351500002PubMedID: 28619700OAI: oai:DiVA.org:liu-140808DiVA, id: diva2:1140808
Note

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

Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-04-20

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Spreco, ArminEriksson, OlleDahlström, ÖrjanTimpka, Toomas
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