Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia

Objective: To develop a forecasting model for the incidence of dengue cases in Subang Jaya\udusing time series analysis.\udMethods: The model was performed using the Autoregressive Integrated Moving Average (ARIMA)\udbased on data collected from 2005 to 2010. The fitted model was then used to predict dengue\udincidence for the year 2010 by extrapolating dengue patterns using three different approaches\ud(i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate\udvariable was computed over a range of lags in order to identify significant variables to be included\udas external regressor.\udResults: The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely\uddescribed the trends of dengue incidence and confirmed the existence of dengue fever cases in\udSubang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)\ud(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the\udtraining data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0)\ud(0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the\uddengue cases for the year 2010.\udConclusions: The ARIMA model with weekly variation is a useful tool for disease control and\udprevention program as it is able to effectively predict the number of dengue cases in Malaysi