Karami Manoochehr, Soori Hamid, Mehrabi Yadollah, Haghdoost Ali Akbar, Gouya Mohammad Mehdi
Department of Epidemiology, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Res Health Sci. 2012;12(1):25-30.
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.
The EWMA algorithms (both EWMA1 with lambda = 0.3 and EWMA2 with lambda = 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.
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 outbreak was better than EWMA1 algorithm.
Applying the EWMA algorithm as an outbreak detection method might not be useful in timely detection of the local outbreaks.
很少有已发表的研究使用实际数据测试来检验疫情检测方法的性能。本研究的目的是确定指数加权移动平均法（EWMA）在实时检测伊朗东部马什哈德市局部疫情中的性能。
将EWMA算法（λ = 0.3的EWMA1和λ = 0.6的EWMA2）应用于麻疹疑似病例的每日计数，以检测2010年期间在马什哈德市发生的实际疫情。采用实际数据测试方法评估EWMA算法的性能，并通过相关分析进行报告。
使用EWMA算法作为疫情检测方法，检测出马什哈德疫情时延迟了约2至7天。此外，EWMA2算法在实时检测疫情方面的效用优于EWMA1算法。
将EWMA算法用作疫情检测方法可能无助于及时检测局部疫情。
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