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Early Prediction of Seasonal Influenza using School Absenteeism

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Title: Early Prediction of Seasonal Influenza using School Absenteeism
Author: Stanley, Anu
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Deardon, RobFeng, Zeny
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.
URI: http://hdl.handle.net/10214/8656
Date: 2014-12


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