Assessing the effectiveness of the Early Aberration Reporting System (EARS) for early event detection of the H1N1 ("SWINE FLU") virus
10Sep_Hagen.pdf (1.082Mb)Abstract
The Monterey County Health Department (MCHD) in California uses the Early Aberration Reporting System (EARS) to monitor emergency room and clinic data for biosurveillance, particularly as an alert system for various types of disease outbreaks. The flexibility of the system has proven to be a very useful feature of EARS; however, little research has been conducted to assess its performance. In this thesis, a quantitative analysis based on modifications to EARS' internal logic and algorithms is assessed. Logic is used as a counting tool for potential cases of outbreak, and the Early Event Detection (EED) algorithms are used to determine whether or not an outbreak is about to occur. The EED methods are compared by assessing their ability to detect the presence of a known H1N1 outbreak in Monterey County. This research found the cumulative sum (CUSUM) detection method to be the most reliable in signaling the H1N1 outbreak, across all combinations of logic explored.
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