comparing statistical models to predict dengue fever notifications
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2012
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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.
| Reference Key |
earnest2012computationalcomparing
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|---|---|
| Authors | ;Arul Earnest;Say Beng Tan;Annelies Wilder-Smith;David Machin |
| Journal | advanced functional materials |
| Year | 2012 |
| DOI |
10.1155/2012/758674
|
| URL | |
| Keywords |
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