Integration of data analysis methods in syndromic surveillance systems

Dimitrios Zikos, Marianna Diomidous

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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
Pages1114-1116
Number of pages3
Volume180
DOIs
Publication statusPublished - 2012
Event24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy
Duration: 26 Aug 201229 Aug 2012

Other

Other24th Medical Informatics in Europe Conference, MIE 2012
CountryItaly
CityPisa
Period26/08/1229/08/12

Keywords

  • Methods
  • Public health
  • Statistical analysis
  • Syndromic surveillance

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  • Cite this

    Zikos, D., & Diomidous, M. (2012). Integration of data analysis methods in syndromic surveillance systems. In Quality of Life Through Quality of Information - Proceedings of MIE 2012 (Vol. 180, pp. 1114-1116) https://doi.org/10.3233/978-1-61499-101-4-1114