Integration of data analysis methods in syndromic surveillance systems

Dimitrios Zikos; Marianna Diomidous

doi:10.3233/978-1-61499-101-4-1114

Integration of data analysis methods in syndromic surveillance systems

Dimitrios Zikos, Marianna Diomidous

Health Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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 language	English
Title of host publication	Quality of Life Through Quality of Information - Proceedings of MIE 2012
Publisher	IOS Press
Pages	1114-1116
Number of pages	3
ISBN (Print)	9781614991007
DOIs	https://doi.org/10.3233/978-1-61499-101-4-1114
State	Published - 2012
Event	24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy Duration: Aug 26 2012 → Aug 29 2012

Publication series

Name	Studies in Health Technology and Informatics
Volume	180
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	24th Medical Informatics in Europe Conference, MIE 2012
Country/Territory	Italy
City	Pisa
Period	08/26/12 → 08/29/12

Keywords

Methods
Public health
Statistical analysis
Syndromic surveillance

Access to Document

10.3233/978-1-61499-101-4-1114

Cite this

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title = "Integration of data analysis methods in syndromic surveillance systems",

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.",

keywords = "Methods, Public health, Statistical analysis, Syndromic surveillance",

author = "Dimitrios Zikos and Marianna Diomidous",

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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. Studies in Health Technology and Informatics, vol. 180, IOS Press, pp. 1114-1116, 24th Medical Informatics in Europe Conference, MIE 2012, Pisa, Italy, 08/26/12. https://doi.org/10.3233/978-1-61499-101-4-1114

Integration of data analysis methods in syndromic surveillance systems. / Zikos, Dimitrios; Diomidous, Marianna.

Quality of Life Through Quality of Information - Proceedings of MIE 2012. IOS Press, 2012. p. 1114-1116 (Studies in Health Technology and Informatics; Vol. 180).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

KW - Methods

KW - Public health

KW - Statistical analysis

KW - Syndromic surveillance

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BT - Quality of Life Through Quality of Information - Proceedings of MIE 2012

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Integration of data analysis methods in syndromic surveillance systems

Abstract

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Keywords

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