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dc.contributor.authorYeng, Prosper
dc.contributor.authorWoldaregay, Ashenafi Zebene
dc.contributor.authorSolvoll, Terje Geir
dc.contributor.authorHartvigsen, Gunnar
dc.identifier.citationJMIR Public Health and Surveillance. 2020, 6 (2), e11512-?.en_US
dc.description.abstractBackground: The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective: Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods: We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results: The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions: The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.en_US
dc.publisherJMIR Publicationsen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleCluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Developmenten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalJMIR Public Health and Surveillanceen_US
dc.description.localcode©Prosper Kandabongee Yeng, Ashenafi Zebene Woldaregay, Terje Solvoll, Gunnar Hartvigsen. Originally published in JMIR Public Health and Surveillance (, 26.05.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.en_US

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