Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jul 6, 2018
Open Peer Review Period: Jul 9, 2018 - Jul 23, 2018
Date Accepted: Feb 6, 2020
Date Submitted to PubMed: May 1, 2020
(closed for review but you can still tweet)
A Systematic Review and Framework of Cluster Detection Mechanism for Syndromic Disease Surveillance systems
ABSTRACT
Background:
Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. In the right mix of cluster detection, big data from self-management of diabetes, internet availability and the prevailing pervasiveness of devices, it is feasible and efficient to detect infectious disease outbreak as early as the incubation stage by using the vulnerability of diabetes patients as a sensor. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework and prototype to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered.
Objective:
The general objective of this studies is to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework and prototype to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered.
Methods:
To this end, we conducted a systematic review exploring different online scholarly databases. Considering peer reviewed journals and articles, literatures search was conducted between January and March 2018.
Results:
Relevant literature were identified using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria and a full text review were done for literature that were found to be relevant. A total of 28 articles were included in the study. The result indicates that various clustering and aberration detection algorithms have been developed and tested up to the task. A framework for cluster detection mechanism in EDMON and other similar syndromic surveillance systems have been developed.
Conclusions:
The study revealed Space-Time Permutation Scan Statistics as the most implemented algorithm. The uniqueness and efficiency of STPSS is that its baseline or expected count is based on its detected cases within a defined geographical distance (cylinder radius) and temporal window (cylinder height). This approach provides significant trend of baseline data while avoiding inclusion of historical data that is irrelevant to the current period. Guided with results from the review, a framework for syndromic surveillance has been developed. Privacy preserving policies and high computational power requirement were found challenging since it restrict usage of specific locations for syndromic surveillance.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.