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

Prosper Kandabongee Yeng,Gunnar Hartvigsen,Ashenafi Zebene Woldaregay,Terje Solvoll

doi:10.2196/11512

Abstract

BackgroundThe 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.ObjectiveOur 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

Highlights

BackgroundLate detection of disease outbreaks has long been a threat to global health security, costing the world many lives, resources, fear, and panic
Traditional surveillance systems are mostly passive and rely on laboratory confirmations to detect disease outbreaks. These have been enhanced by syndromic surveillance systems [4], which largely depend on visible signs and symptoms and data sources including emergency department records [5], school absenteeism, work absenteeism, disease reporting systems, and over-the-counter medication sales [6,7]
The results are targeted at people who need to implement efficient syndromic surveillance systems for applications such as over-the-counter medication, school and work absenteeism, and disease surveillance relating to presymptomatic stages, among others

Summary

Introduction

Late detection of disease outbreaks has long been a threat to global health security, costing the world many lives, resources, fear, and panic. Traditional surveillance systems are mostly passive and rely on laboratory confirmations to detect disease outbreaks. 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

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Methods

Results

Discussion

Conclusion