Evaluating the Performance of the Exponentially Weighted Moving Average in Measles Outbreak Detection using Real Data Testing

Evaluating the performance of outbreak detection methods using real data testing provide the highest degree of validity. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in real time detection of two local outbreaks in Iran. The EWMA algorithm (both ƛ= 0.3 and 0.6) applied on daily counts of suspected cases of measles to detect local outbreaks which had been occurred in Mashhad and Bandar Abbas cities during 2010. The performance of The EWMA algorithms were evaluated using real data testing approach and reported by correlation analysis. Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method while the utility of EWMA algorithms in real time detection of Bandar Abbas'' outbreak were on time good optimal. Maximum correlation value for EWMA 2 in relation to Mashhad outbreak was 0.60 at lag 2. Applying the EWMA algorithm as an outbreak detection method at local levels is not suggested. However the characteristics of data are determinant of the performance of such detection methods.