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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 758674, 6 pages
http://dx.doi.org/10.1155/2012/758674
Research Article

Comparing Statistical Models to Predict Dengue Fever Notifications

1Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School Singapore, Singapore 169857
2Tan Tock Seng Hospital, Singapore 308433
3Institute of Public Health, University of Heidelberg, Germany
4National University of Singapore, Singapore 119077
5University of Leicester, UK
6University of Sheffield, UK

Received 16 September 2011; Revised 7 December 2011; Accepted 8 December 2011

Academic Editor: Chris Bauch

Copyright © 2012 Arul Earnest et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the female Aedes aegypti mosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.