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Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
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
2016 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 6, no 5, e010683- p.Article, review/survey (Refereed) Published
Resource type
Text
Abstract [en]

Objectives Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. Design The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. Results Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. Conclusions Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

Place, publisher, year, edition, pages
BMJ PUBLISHING GROUP , 2016. Vol. 6, no 5, e010683- p.
Keyword [en]
influenza; detection algorithms; prediction algorithms; evaluation; meta-narrative review
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
URN: urn:nbn:se:liu:diva-130311DOI: 10.1136/bmjopen-2015-010683ISI: 000378414700068PubMedID: 27154479OAI: oai:DiVA.org:liu-130311DiVA: diva2:950490
Note

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

Available from: 2016-07-31 Created: 2016-07-28 Last updated: 2017-11-28
In thesis
1. Epidemiological and statistical basis for detection and prediction of influenza epidemics
Open this publication in new window or tab >>Epidemiological and statistical basis for detection and prediction of influenza epidemics
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A large number of emerging infectious diseases (including influenza epidemics) has been identified during the last century. The emergence and re-emergence of infectious diseases have a negative impact on global health. Influenza epidemics alone cause between 3 and 5 million cases of severe illness annually, and between 250,000 and 500,000 deaths. In addition to the human suffering, influenza epidemics also impose heavy demands on the health care system. For example, hospitals and intensive care units have limited excess capacity during infectious diseases epidemics. Therefore, it is important that increased influenza activity is noticed early at local levels to allow time to adjust primary care and hospital resources that are already under pressure. Algorithms for the detection and prediction of influenza epidemics are essential components to achieve this.

Although a large number of studies have reported algorithms for detection or prediction of influenza epidemics, outputs that fulfil standard criteria for operational readiness are seldom produced. Furthermore, in the light of the rapidly growing availability of “Big Data” from both diagnostic and prediagnostic (syndromic) data sources in health care and public health settings, a new generation of epidemiologic and statistical methods, using several data sources, is desired for reliable analyses and modeling.

The rationale for this thesis was to inform the planning of local response measures and adjustments to health care capacity during influenza epidemics. The overall aim was to develop a method for detection and prediction of influenza epidemics. Before developing the method, three preparatory studies were performed. In the first of these studies, the associations (in terms of correlation) between diagnostic and pre-diagnostic data sources were examined, with the aim of investigating the potential of these sources for use in influenza surveillance systems. In the second study, a literature study of detection and prediction algorithms used in the field of influenza surveillance was performed. In the third study, the algorithms found in the previous study were compared in a prospective evaluation study. In the fourth study, a method for nowcasting of influenza activity was developed using electronically available data for real-time surveillance in local settings followed by retrospective application on the same data. This method includes three functions: detection of the start of the epidemic at the local level and predictions of the peak timing and the peak intensity. In the fifth and final study, the nowcasting method was evaluated by prospective application on authentic data from Östergötland County, Sweden.

In the first study, correlations with large effect sizes between diagnostic and pre-diagnostic data were found, indicating that pre-diagnostic data sources have potential for use in influenza surveillance systems. However, it was concluded that further longitudinal research incorporating prospective evaluations is required before these sources can be used for this purpose. In the second study, a meta-narrative review approach was used in which two narratives for reporting prospective evaluation of influenza detection and prediction algorithms were identified: the biodefence informatics narrative and the health policy research narrative. As a result of the promising performances of one detection algorithm and one prediction algorithm in the third study, it was concluded that both further evaluation research and research on methods for nowcasting of influenza activity were warranted. In the fourth study, the performance of the nowcasting method was promising when applied on retrospective data but it was concluded that thorough prospective evaluations are necessary before recommending the method for broader use. In the fifth study, the performance of the nowcasting method was promising when prospectively applied on authentic data, implying that the method has potential for routine use. In future studies, the validity of the nowcasting method must be investigated by application and further evaluation in multiple local settings, including large urbanizations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 102 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1566
National Category
Biomedical Laboratory Science/Technology Bioinformatics and Systems Biology Computer Science Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:liu:diva-136553 (URN)10.3384/diss.diva-136553 (DOI)9789176855690 (ISBN)
Public defence
2017-05-19, Belladonnan, Campus US, Linköping, 09:00 (English)
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Supervisors
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-20Bibliographically approved

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