中国全科医学2018，Vol.21Issue(9)：1098-1103,6.DOI:10.3969/j.issn.1007-9572.2017.00.267
基于经验模态分解的我国布鲁菌病月发病率预测研究
Prediction for Monthly Incidence of Brucellosis in China based on Empirical Mode Decomposition
摘要
Abstract
Objective To develop a model for the prediction of monthly incidence of brucellosis in China based on empirical mode decomposition (EMD) and time series analysis results of the volatility characteristics of the incidence of brucellosis in China during 2004—2016, and use it to predict the monthly incidence of brucellosis in China in 2017. Methods From the websites of Data-center of China Public Health Science and Bureau of Disease Prevention and Control, National Health and Family Planning Commission of the PRC, we collected the data about the incidence of brucellosis in China from January 2004 to December 2016 and calculated the monthly incidence of brucellosis during this period. We used the data from 2004 to 2015 as training data to develop the model for the prediction of monthly incidence of brucellosis in China and adopted the data between January and December 2016 as test data to verify the model. Then, the intrinsic mode functions IMF1-IMF4 and trend term r were generated by EMD. Support vector machine was used to model IMF1-IMF4 components, and ARIMA to trend term r. The monthly incidence of brucellosis was finally obtained by weighting the model predicted values linearly. Results The range of penalty parameter c and kernel function parameter g of SVM model were 0.088 4-100.000 0 and 0.010 0-128.000 0, respectively. In the ARIMA((1,12,24),1,0) model, constant term and the autoregressive coefficient of first-order lag, twelfth-order lag and twenty-fourth-order lag were 0.002 003, 1.087 788, -0.145 494 and 0.028 783, respectively. For predicting the monthly incidence of brucellosis in China between January and November 2016, the root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE) of proposed method were 0.020 1, 0.016 9, 0.066 5, respectively; the errors of single SVM model based on undecomposed sequence were 0.072 2, 0.056 0, 0.197 5, respectively; the errors of single ARIMA model based on undecomposed sequence were 0.165 0, 0.156 2, 0.610 0, respectively. In addition, the monthly incidence of brucellosis in 2017 was predicted to be 0.287 0/100 thousand people-0.372 6/100 thousand people according to the proposed method. Conclusion The model for the prediction of monthly incidence of brucellosis in China based on the related incidence data using EMD and time series analysis has high prediction accuracy with minor prediction error. The mean monthly incidence of brucellosis in 2017 was predicted to be about 0.35/100 thousand people.关键词
布鲁杆菌病/发病率/预测/经验模态分解/支持向量机/自回归移动平均模型Key words
Brucellosis/Incidence/Forecasting/Empirical mode decomposition/Support vector machine/ARIMA分类
医药卫生引用本文复制引用
乔贺倩,李维德,于国伟..基于经验模态分解的我国布鲁菌病月发病率预测研究[J].中国全科医学,2018,21(9):1098-1103,6.基金项目
国家自然科学基金资助项目(41571016) （41571016）
中央高校基本科研业务费专项资金(lzujbky-2017-166) （lzujbky-2017-166）
