Real time detection of a measles outbreak using the exponentially weighted moving average: does it work?
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 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.


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 outbreak was better than EWMA1 algorithm.


CONCLUSIONS
Applying the EWMA algorithm as an outbreak detection method might not be useful in timely detection of the local outbreaks.
Department of Epidemiology, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Manoochehr
M
Karami
Hamid
H
Soori
Yadollah
Y
Mehrabi
Ali Akbar
AA
Haghdoost
Mohammad Mehdi
MM
Gouya
D000465:Algorithms; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D006801:Humans; D007492:Iran / Q000453:epidemiology; D008457:Measles / Q000453:epidemiology*; D011159:Population Surveillance / Q000379:methods*