![]() In spite of often being non-specific to community illness, these methods tend to have a level of sensitivity that can allow them to identify disease outbreaks, or the start of a seasonal epidemic, earlier than typical surveillance methods that monitor laboratory-reported cases (which are often lagged), thereby making it possible for public health agencies to respond quickly. Syndromic surveillance methods focus on behaviours that occur due to symptoms, such as non-prescription drug sales, absenteeism from work or school, and web queries. Early epidemic detection at the regional level can be conducive in reducing the impact of influenza by prompting public health authorities to implement mitigating strategies. Seasonal influenza epidemics cause significant morbidity and mortality each year, with the duration and severity of influenza epidemics varying year-to-year. Models selected by either FATQ or AATQ would more effectively predict community influenza activity with the local community than those selected by FAR. The FATQ-selected model raised acceptable first alerts most frequently, while the AATQ-selected model raised first alerts within the ideal range most frequently. A simulation study that mimicked the WDG population and influenza demographics was conducted for further evaluation of the proposed metric. Daily elementary school absenteeism and laboratory-confirmed influenza case data collected by WDGPH were used for demonstration and evaluation of the proposed metric. Alerts raised by ATQ and FAR selected models were compared. ![]() Summary statistics of ATQ, average alert time quality (AATQ) and first alert time quality (FATQ), were used for model evaluation and selection. The ATQ assessed alerts on a gradient, where alerts raised incrementally before or after an optimal day were considered informative, but were penalized for lack of timeliness. The alert time quality (ATQ) metric is investigated as a model selection criterion on both a simulated and real data set. Here, a new metric that simultaneously evaluates epidemic alert accuracy and timeliness is proposed. ![]() However model evaluation and selection was primarily based on alert accuracy, measured by the false alert rate (FAR), and failed to optimize timeliness. A recent study indicated that model-based alternatives, such as distributed lag seasonal logistic regression models, provided improved alerts for detecting an upcoming epidemic. Wellington-Dufferin-Guelph Public Health (WDGPH) has conducted an absenteeism-based influenza surveillance program in the WDG region of Ontario, Canada since 2008, using a 10% absenteeism threshold to raise an alert for the implementation of mitigating measures. ![]()
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