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.
authors.affiliation: Faculty of Nursing, University of Athens, Athens, Greece. dimitriszikos@gmail.com
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Zikos
authors.forename: Marianna
authors.initials: M
authors.lastname: Diomidous
mesh_terms: D016022:Case-Control Studies; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data*; D006115:Greece / Q000453:epidemiology; D006801:Humans; D051188:Knowledge Bases; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D016016:Proportional Hazards Models; D013577:Syndrome; D018511:Systems Integration