A systematic review of cluster detection mechanisms in syndromic surveillance: Towards developing a framework of cluster detection mechanisms for EDMON system

Prosper Kandabongee Yeng
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Ashenafi Zebene Woldaregay
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Terje Solvoll
Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

Gunnar Hartvigsen
Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Ladda ner artikel

Ingår i: Proceedings from The 16th Scandinavian Conference on Health Informatics 2018, Aalborg, Denmark August 28–29, 2018

Linköping Electronic Conference Proceedings 151:11, s. 62-69

Visa mer +

Publicerad: 2018-08-24

ISBN: 978-91-7685-213-2

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


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. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework 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. 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. Relevant literatures were identified using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria and a full text review were done for literatures 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. In this regard, privacy preserving policies and high computational power requirement were found challenging since it restrict usage of specific locations for syndromic surveillance.


Syndromic Surveillance, Spatiotemporal Clustering, Smart Phone, Aberration Detection.


[1] WHO. Ebola Virus Disease. 2017 June 2017 [cited 2018 20/01/2018]; Available from: http://www.who.int/mediacentre/factsheets/fs103/en/.

[2] Daulaire, N.M., Global Health Security. 2018.

[3] Hope, K., et al., Syndromic surveillance: is it a useful tool for local outbreak detection?, in J Epidemiol Community Health. 2006. p. 374-5.

[4] Choi, J., et al., Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health, 2016. 16(1): p. 1238

[5] Nie, S., et al., Real-Time Monitoring of School Absenteeism to Enhance Disease Surveillance: A Pilot Study of a Mobile Electronic Reporting System, in JMIR Mhealth Uhealth. 2014.

[6] Woldaregay, A.Z., et al. EDMON-A Wireless Communication Platform for a Real-Time Infectious Disease Outbreak De-tection System Using Self-Recorded Data from People with Type 1 Diabetes. In Proceedings from The 15th Scandinavian Conference on Health Informatics 2017 Kristiansand, Norway, August 29–30, 2017. 2018. Linköping University Electronic Press.

[7] Heffernan, R., et al., Syndromic surveillance in public health practice, New York City. Emerg Infect Dis, 2004. 10(5): p. 858-64,15200820,

[8] Jacquez, G., Spatial Clustering and Autocorrelation in Health Events | SpringerLink. 2018

[9] Woldaregay, A., et al., An Early Infectious Disease Outbreak Detection Mechanism Based on Self-Recorded Data from People with Diabetes. Studies in health technology and informatics, 2017. 245: p. 619-623

[10] Wang, H. and U.o.S.C.-. Columbia, Pattern Extraction From Spatial Data - Statistical and Modeling Approches. 2014, University of South Carolina.

[11] MedicineNet, Modeling Infectious Diseases in Humans and Animals. 2017.

[12] Study.com. Progress of Disease: Infection to Recovery - Video & Lesson Transcript | Study.com. 2018; Available from: http://study.com/academy/lesson/progress-ofdisease-infection-to-recovery.html.

[13] Marshall, J.B., et al., Prospective Spatio-Temporal Surveillance Methods for the Detection of Disease Clusters. 2009

[14] Martin Kulldorff, R.H., Jessica Hartman, Renato Assunção, Farzad Mostashari, A Space–Time Permutation Scan Statistic for Disease Outbreak Detection. 2005

[15] Fanaee-T, H., Spatio-Temporal Clustering Methods Classification (PDF Download Available), in Doctoral Symposium on Informatics Engineering. 2012.

[16] P.N. Tan, Vipin Kumar, and M. Steinbach, Cluster Analysis: Basic Concepts and Algorithms. 2005

[17] Birant, D. and A. Kut, ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering, 2007. 60(1): p. 208-221

[18] Hutwagner, L., et al., Comparing Aberration Detection Methods with Simulated Data, in Emerg Infect Dis. 2005. p. 314-6.

[19] Chan, T.C., Y.C. Teng, and J.S. Hwang, Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models, in BMC Public Health. 2015.

[20] Kleinman, K.P., et al., A model-adjusted space-time scan statistic with an application to syndromic surveillance. Epidemiol Infect, 2005. 133(3): p. 409-19,15962547,2870264.

[21] Kulldorff, M., A spatial scan statistic. http://dx.doi.org/10.1080/03610929708831995, 2007

[22] Chen, D., et al., Spatial and temporal aberration detection methods for disease outbreaks in syndromic surveillance systems. http://dx.doi.org/10.1080/19475683.2011.625979, 2011

[23] Khokhar, S. and A.A. Nilsson. Introduction to Mobile Trajectory Based Services: A New Direction in Mobile Location Based Services. in Wireless Algorithms, Systems, and Applications. 2009. Berlin, Heidelberg: Springer Berlin Heidelberg.

[24] Jeung†, H., et al., Discovery of Convoys in Trajectory Databases. 2008

[25] Sharip, A., Preliminary Analysis of SaTScan’s Effectiveness to Detect Known Disease Outbreaks Using Emergency Department Syndromic Data in Los Angeles County. 2006.

[26] Kajita, E., et al., Harnessing Syndromic Surveillance Emergency Department Data to Monitor Health Impacts During the 2015 Special Olympics World Games. Public Health Rep, 2017. 132(1_suppl): p. 99s-105s,28692391,PMC5676508.

[27] PRISMA. PRISMA. 2018; Available from: http://www.prisma-statement.org/.

[28] Omicsonline. Inclusion and Exclusion Criteria and Rationale. 2018; Available from: https://www.omicsonline.org/articles-images/2157-7595-5-183-t001.html.

[29] Ali, M.A., et al., ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan. Public Health, 2016. 134: p. 72-85,26880489,

[30] Groeneveld, G.H., et al., ICARES: a real-time automated detection tool for clusters of infectious diseases in the Netherlands. BMC Infect Dis, 2017. 17(1): p. 201,28279150,PMC5345172.

[31] GDPR, E. EU GDPR Information Portal. 2018; Available from: http://eugdpr.org/eugdpr.org.html.

[32] Yan, W., et al., ISS--an electronic syndromic surveillance system for infectious disease in rural China. PLoS One, 2013. 8(4): p. e62749,23626853,PMC3633833.

[33] Khanita Duangchaemkarn Varin, C.P., Wiwatanadate, Symptom-based data preprocessing for the detection of disease outbreak - IEEE Conference Publication, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2017: Seogwipo. p. 2614-2617.

[34] Takahashi, K., et al., A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring. International Journal of Health Geographics, 2008. 7(1): p. 14

[35] Yih, W.K., et al., Evaluating real-time syndromic surveillance signals from ambulatory care data in four states. Public Health Rep, 2010. 125(1): p. 111-20,20402203,PMC2789823.

[36] Hutwagner, L., et al., The bioterrorism preparedness and response Early Aberration Reporting System (EARS). J Urban Health, 2003. 80(2 Suppl 1): p. i89-96,12791783,PMC3456557.

[37] Cesario, M., et al., Time-based Geographical Mapping of Communicable Diseases - IEEE Conference Publication. 2012

[38] Qi, F. and F. Du, Tracking and visualization of spacetime activities for a micro-scale flu transmission study. International Journal of Health Geographics, 2013. 12(1): p. 6

[39] Nicholas Thapen, et al., DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response. 2016

[40] Edanz Group Japan K.K., Writing Point: How to Write About Your Study Limitations Without Limiting Your Impact | Edanz Editing. 2015

Citeringar i Crossref