Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China

Abstract

BackgroundOutbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.MethodIn this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.ResultsThe best-fitted hybrid model was combined with seasonal ARIMA and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively −965.03, −1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.ConclusionThe model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

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“…ARIMA attempts to predict the future values based on auto-regressive and moving average techniques [63,64]. Unlike ARIMA, that focuses only on a few number of past instances observed just before, S-ARIMA exploits a larger number of past instances to consider the seasonal patterns throughout them [65]. The parameters for the two models were set to be the same as presented in [64,66].…”

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“…In the original training dataset, we used four of the twelve original samples to train the network, so that the N 2 (non-linear part of Z 2 dataset, which is the training set) group could be fitted. Finally, the estimated values from the neural network N are then added to the estimated ARIMA values, so we have the final estimation model for Z, which is given by equation (6).…”

“…Some other studies related to what we propose in this work can be seen in [3][4][5][6][7][8][9][10][11]. In the study by Liangping and Sternberg, two approaches are proposed to predict the Peak Signal-to-Noise Ratio (PSNR) in video transmissions.…”

“…e results were compared with the single ARIMA and single ANN estimates. e authors of [6] propose a hybrid SARIMA (Seasonal ARIMA) and nonlinear autoregressive neural network (NARNN) for forecasting the incidence of hand-foot-and-mouth disease in Chenzen, China.…”

Tracking and predicting hand, foot, and mouth disease (HFMD) epidemics in China by Baidu queries

“…average (ARIMA) model [1,6,[8][9][10][11]. Pan et al found the ARIMA model preferable to the GM (1,1) gray system model in predicting HFMD by comparing their outputs [6].…”