Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

Abstract

BackgroundHepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease.MethodsThe autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models.ResultsThe morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models.ConclusionsThe hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County.

Estimation of COVID-19 prevalence in Italy, Spain, and France

“…Disease Method(s) (Guan et al, 2004) HAV ARIMA, ANNs (Earnest et al, 2005) SARS ARIMA (Gaudart et al, 2009) Malaria ARIMA (Liu et al, 2011) HFRS ARIMA (Zhang et al, 2013) Typhoid Fever SARIMA, BPNN, RBFNN, and ERNN (Ren et al, 2013) HEV ARIMA, BPNN (Nsoesie et al, 2013) HPS ARIMA (Zheng et al, 2015) Tuberculosis ARIMA (Wu et al, 2015) HFRS ARIMA, GRNN, and NARNN (Zeng et al, 2016) Pertussis ARIMA, ETS (Wei et al, 2016) Hepatitis ARIMA, GRNN (Sun et al, 2018) SFTS ARIMA, NBM, and GAM (Wang et al, 2018a and France over the next ten days with 95% relative confidence intervals.…”

Predicting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models in 2020

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Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities

“…In this study, we use RBF with two layers from an internal generalized regressor function of MATLAB (newgrnn). It has a Gaussian activation function, and the network estimate N is given by equation (5).…”

“…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.…”

“…It is one of the few works that use this type of modelling to tackle a problem of this kind. In [5], a hybrid ARIMA-ANN (where the ANN works as a generalized regressor) is proposed to predict the incidence of hepatitis in Heng County, China. e results were compared with the single ARIMA and single ANN estimates.…”