Back propagation neural network and non-linear regression models for dengue outbreak prediction

Abstract

Malaysia has a good dengue surveillance system but there have been insufficient findings on suitable model to predict future dengue outbreak since conventional method is still being used. This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different architectures and parameters incorporating time series, location and rainfall data to define the best architecture for early prediction of dengue outbreak. The case study covered dengue and rainfall data of five districts in Selangor from year 2004 until 2005. Four architectures of NNM and NLRM were developed in this study. Architecture I involved only dengue cases data, Architecture II involved combination of dengue cases data and rainfall data, Architecture III involved proximity location dengue cases data, while Architecture IV involved the combination of all criteria. The C programming and Matlab software were used by this artificial intelligent method to develop the NNM and NLRM. The parameters studied in this research were adjusted for optimal performance. These parameters are the learning rate, momentum rate and number of neurons in the hidden layer of architectures. The performance of overall architecture was analyzed and the result shows that the Mean Square Error (MSE) for all architectures by using NNM is better compared to NLRM. Furthermore, the results also indicate that architecture IV performs significantly better than other architectures in predicting dengue outbreak using NNM compared with NLRM. It is therefore proposed as a useful approach in the problem of time series prediction of dengue outbreak. These results can help government especially for Vector Borne Disease Control (VBDC) Section of Health Ministry to develop a contingency plan to mobilize expertise, vaccines and other supplies and equipment that may be necessary in order to face dengue epidemic issues.