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

School of Life and Health Sciences

National and Kapodistrian University of Athens

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

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
Pages	1114-1116
Number of pages	3
Volume	180
DOIs	https://doi.org/10.3233/978-1-61499-101-4-1114
Publication status	Published - 2012
Event	24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy Duration: 26 Aug 2012 → 29 Aug 2012

Other

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

Keywords

Methods
Public health
Statistical analysis
Syndromic surveillance

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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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",

year = "2012",

doi = "10.3233/978-1-61499-101-4-1114",

language = "English",

isbn = "9781614991007",

volume = "180",

pages = "1114--1116",

booktitle = "Quality of Life Through Quality of Information - Proceedings of MIE 2012",

note = "24th Medical Informatics in Europe Conference, MIE 2012 ; Conference date: 26-08-2012 Through 29-08-2012",

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T1 - Integration of data analysis methods in syndromic surveillance systems

AU - Zikos, Dimitrios

AU - Diomidous, Marianna

PY - 2012

Y1 - 2012

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

UR - https://www.scopus.com/pages/publications/84872564226

U2 - 10.3233/978-1-61499-101-4-1114

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

M3 - Conference contribution

C2 - 22874371

AN - SCOPUS:84872564226

SN - 9781614991007

VL - 180

SP - 1114

EP - 1116

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

T2 - 24th Medical Informatics in Europe Conference, MIE 2012

Y2 - 26 August 2012 through 29 August 2012

ER -

Integration of data analysis methods in syndromic surveillance systems

Abstract

Other

Keywords

UN SDGs

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