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Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

BACKGROUND: 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 th...

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Autores principales: Yeng, Prosper Kandabongee, Woldaregay, Ashenafi Zebene, Solvoll, Terje, Hartvigsen, Gunnar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284413/
https://www.ncbi.nlm.nih.gov/pubmed/32357126
http://dx.doi.org/10.2196/11512
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author Yeng, Prosper Kandabongee
Woldaregay, Ashenafi Zebene
Solvoll, Terje
Hartvigsen, Gunnar
author_facet Yeng, Prosper Kandabongee
Woldaregay, Ashenafi Zebene
Solvoll, Terje
Hartvigsen, Gunnar
author_sort Yeng, Prosper Kandabongee
collection PubMed
description BACKGROUND: 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.
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spelling pubmed-72844132020-06-19 Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development Yeng, Prosper Kandabongee Woldaregay, Ashenafi Zebene Solvoll, Terje Hartvigsen, Gunnar JMIR Public Health Surveill Review BACKGROUND: 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. JMIR Publications 2020-05-26 /pmc/articles/PMC7284413/ /pubmed/32357126 http://dx.doi.org/10.2196/11512 Text en ©Prosper Kandabongee Yeng, Ashenafi Zebene Woldaregay, Terje Solvoll, Gunnar Hartvigsen. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 26.05.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), 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 http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Review
Yeng, Prosper Kandabongee
Woldaregay, Ashenafi Zebene
Solvoll, Terje
Hartvigsen, Gunnar
Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title_full Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title_fullStr Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title_full_unstemmed Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title_short Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development
title_sort cluster detection mechanisms for syndromic surveillance systems: systematic review and framework development
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284413/
https://www.ncbi.nlm.nih.gov/pubmed/32357126
http://dx.doi.org/10.2196/11512
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