Integration of data analysis methods in syndromic surveillance systems

Dimitrios Zikos, Marianna Diomidous

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Syndromic surveillance systems perform real-time analysis of health data to enable early identification of potential public health threats, evaluating whether distributional parameters have been increased beyond a threshold. This paper presents the applied data analysis methods in five non-industrial surveillance systems. Four time series and spatial cluster analysis methods were found to be implemented: SMART, EWMA, CuSum and WSARE. Combined use both spatial and time series methods is found in the presented surveillance applications. Data analysis methods for syndromic surveillance are a constantly emerging field, while new statistical methods and algorithms are implemented into surveillance systems.

Original languageEnglish
Title of host publicationQuality of Life Through Quality of Information - Proceedings of MIE 2012
PublisherIOS Press
Number of pages3
ISBN (Print)9781614991007
StatePublished - 2012
Event24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy
Duration: Aug 26 2012Aug 29 2012

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference24th Medical Informatics in Europe Conference, MIE 2012


  • Methods
  • Public health
  • Statistical analysis
  • Syndromic surveillance


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