Real Time Detection of a Measles Outbreak using the Exponentially Weighted Moving Average: Does it Work?

Manoochehr Karami, Hamid Soori, Yadollah Mehrabi, Ali Akbar Haghdoost, Mohammad Mehdi Gouya

Abstract


Background: There are few published studies that use real data testing to examine the performance of outbreak detection methods. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in real time detection of a local outbreak in Mashhad City, eastern Iran.

Methods: The EWMA algorithms (both EWMA1 with λ=0.3 and EWMA2 with λ=0.6) were applied to daily counts of suspected cases of measles to detect real outbreak which has occurred in the city of Mashhad during 2010. The performances of the EWMA algorithms were evaluated using a real data testing approach and reported by correlation analysis.

Results: Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method. Moreover, the utility of EWMA2 algorithm in real time detection of the outbreaks was better than EWMA1 algorithm.

Conclusion: Applying the EWMA algorithm as an outbreak detection method might not be useful in timely detection of the local outbreaks.

Keywords


Disease Outbreak; Measles; Surveillance; Exponentially Weighted Moving Average (EWMA); Iran

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