NDLI: Comparing Statistical Models to Predict Dengue Fever Notifications

Content Provider	PubMed Central
Author	Earnest, Arul Tan, Say Beng Wilder-smith, Annelies Machin, David
Copyright Year	2012
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 vectoreradication are important in controlling the spread of the disease. DF is primarilytransmitted by the female Aedes aegypti mosquito. We compared two statisticalmodels that can be used in the surveillance and forecast of notifiable infectiousdiseases, namely, the Autoregressive Integrated Moving Average (ARIMA) modeland the Knorr-Held two-component (K-H) model. The Mean Absolute PercentageError (MAPE) was used to compare models. We developed the models using useddata on DF notifications in Singapore from January 2001 till December 2006 andthen validated the models with data from January 2007 till June 2008. The K-Hmodel resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMAmodel. We conclude that the models' performances are similar, but we found thatthe K-H model was relatively more difficult to fit in terms of the specification of theprior parameters and the relatively longer time taken to run the models.
Related Links	http://dx.doi.org/10.1155/2012/758674
Starting Page	758674
File Format	PDF
ISSN	1748670X
e-ISSN	17486718
Journal	Computational and Mathematical Methods in Medicine
Volume Number	2012
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2012-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Modelling and Simulation Immunology and Microbiology(all) Applied Mathematics Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Modeling and Simulation

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