Please use this identifier to cite or link to this item: https://doi.org/10.1155/2012/758674
Title: Comparing statistical models to predict dengue fever notifications
Authors: Earnest, A. 
Tan, S.B. 
Wilder-Smith, A.
MacHin, D.
Issue Date: 2012
Citation: Earnest, A., Tan, S.B., Wilder-Smith, A., MacHin, D. (2012). Comparing statistical models to predict dengue fever notifications. Computational and Mathematical Methods in Medicine 2012 : -. ScholarBank@NUS Repository. https://doi.org/10.1155/2012/758674
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. Copyright © 2012 Arul Earnest et al.
Source Title: Computational and Mathematical Methods in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/108897
ISSN: 1748670X
DOI: 10.1155/2012/758674
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