Application of nonlinear autoregressive neural network in predicting incidence tendency of hemorrhagic fever with renal syndrome
10.3760/cma.j.issn.0254-6450.2015.12.017
- VernacularTitle:非线性自回归神经网络在肾综合征出血热流行趋势预测中的应用
- Author:
Wei WU
1
;
Shuyi AN
;
Junqiao GUO
;
Peng GUAN
;
Yangwu REN
;
Lingzi XIA
;
Baosen ZHOU
Author Information
1. 中国医科大学公共卫生学院流行病学教研室
- Keywords:
Hemorrhagic fever with renal syndrome;
Nonlinear autoregressive neural network;
Predict
- From:
Chinese Journal of Epidemiology
2015;36(12):1394-1396
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the prospect of nonlinear autoregressive neural network in fitting and predicting the incidence tendency of hemorrhagic fever with renal syndrome (HFRS),in the mainland of China.Methods Monthly reported case series of HFRS in China from 2004 to 2013 were used to build both ARIMA and NAR neural network models,in order to predict the monthly incidence of HFRS in China in 2014.Fitness and prediction on the effects of these two models were compared.Results For the Fitting dataset,MAE,RMSE and MAPE of the ARIMA model were 148.058,272.077 and 12.678％ respectively,while the MAE,RMSE and MAPE of NAR neural network appeared as 119.436,186.671 and 11.778％ respectively.For the Predicting dataset,MAE,RMSE and MAPE of the ARIMA model appeared as 189.088,221.133 and 21.296％,while the MAE,RMSE and MAPE of the NAR neural network as 119.733,151.329 and 11.431％ respectively.Conclusion The NAR neural network showed better effects in fitting and predicting the incidence tendency of HFRS than using the traditional ARIMA model,in China.NAR neural network seemed to have strong application value in the prevention and control of HFRS.
