| 吴伟,安淑一,郭军巧,关鹏,任仰武,夏玲姿,周宝森.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[J].Chinese journal of Epidemiology,2015,36(12):1394-1396 |
| 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 |
| Application of nonlinear autoregressive neural network in predicting incidence tendency of hemorrhagic fever with renal syndrome |
| Received:March 08, 2015 |
| DOI：10.3760/cma.j.issn.0254-6450.2015.12.017 |
| KeyWord: 肾综合征出血热 非线性自回归神经网络 预测 |
| English Key Word: Hemorrhagic fever with renal syndrome Nonlinear autoregressive neural network Predict |
| FundProject:国家自然科学基金(81202254,30771860) |
| Author Name | Affiliation | E-mail | | Wu Wei | Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China | | | An Shuyi | Liaoning Provincial Center for Disease Control and Prevention | | | Guo Junqiao | Liaoning Provincial Center for Disease Control and Prevention | | | Guan Peng | Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China | | | Ren Yangwu | Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China | | | Xia Lingzi | Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China | | | Zhou Baosen | Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, China | bszhou@mail.cmu.edu.cn |
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| Abstract: |
| 目的 探讨非线性自回归(NAR)神经网络拟合及预测我国HFRS流行趋势的应用。方法 使用2004－2013年全国HFRS月报告发病数序列建立ARIMA模型和NAR神经网络模型,预测2014年HFRS月发病数,并比较两模型的拟合和预测效果。结果 对于拟合集,ARIMA模型的平均绝对误差(MAE)、均方误差平方根(RMSE)和平均绝对误差百分比(MAPE)分别为148.058、272.077和12.678%, NAR神经网络分别为119.436、186.671和11.778%; 对于预测集,ARIMA模型的MAE、RMSE和MAPE分别为189.088、221.133和21.296%, NAR神经网络分别为119.733、151.329和11.431%。结论 NAR神经网络对于全国HFRS流行趋势拟合及预测效果优于传统的ARIMA模型,具有良好推广应用价值。 |
| English 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. |
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