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Early Prediction of Seasonal Influenza using School Absenteeism

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dc.contributor.advisor Deardon, Rob
dc.contributor.advisor Feng, Zeny Stanley, Anu 2015-01-05T21:35:39Z 2015-01-05T21:35:39Z 2014-12 2014-12-08 2015-01-05
dc.description.abstract Syndromic surveillance uses non-traditional health-related data to detect regularly occurring or emerging infectious disease outbreaks. A school absenteeism surveillance system was implemented by Wellington-Dufferin-Guelph Public Health (WDGPH) since February-2008 using an arbitrary 10% absenteeism threshold. The primary focus of this thesis is to refine the current methods to allow early detection of seasonal influenza outbreaks in the community. Surveillance systems were developed linking real outbreaks, defined by aggregated hospital data within the WDG area, to the school absenteeism data. We used the moving average (MA), exponentially weighted moving average (EWMA) and logistic regression (LR) to compute a unique baseline for each school on a given day and compared its false alarm rate (FAR) and accumulated days delay (ADD) to that of a steady baseline currently used by the WDGPH. This study concludes that the current methods of WDGPH appear insufficient in comparison to the surveillance systems implemented in this thesis. en_US
dc.language.iso en en_US
dc.subject Influenza Surveillance en_US
dc.subject School Absenteeism en_US
dc.subject Syndromic Surveillance en_US
dc.title Early Prediction of Seasonal Influenza using School Absenteeism en_US
dc.type Thesis en_US Mathematics and Statistics en_US Master of Science en_US Department of Mathematics and Statistics en_US
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