A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM

作者全名："Wan, Yiran; Song, Ping; Liu, Jiangchen; Xu, Ximing; Lei, Xun"

作者地址："[Wan, Yiran; Lei, Xun] Chongqing Med Univ, Sch Publ Hlth, Chongqing, Peoples R China; [Wan, Yiran; Lei, Xun] Res Ctr Med & Social Dev, Chongqing, Peoples R China; [Wan, Yiran; Lei, Xun] Chongqing Med Univ, Collaborat Innovat Ctr Social Risks Governance Hlt, Chongqing, Peoples R China; [Wan, Yiran; Lei, Xun] Chongqing Med Univ, Res Ctr Publ Hlth Secur, 1 Med Coll Rd, Chongqing 400016, Peoples R China; [Song, Ping; Xu, Ximing] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Childrens Hosp,Key Lab Child Dev & Disorders, Big Data Ctr Childrens Med Care,Minist Educ, 136 Zhongshan 2nd Rd, Chongqing 400014, Peoples R China; [Liu, Jiangchen] Chongqing Normal Univ, Sch Math Sci, Chongqing, Peoples R China"

通信作者："Lei, X (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Chongqing, Peoples R China.; Lei, X (通讯作者)，Res Ctr Med & Social Dev, Chongqing, Peoples R China.; Lei, X (通讯作者)，Chongqing Med Univ, Collaborat Innovat Ctr Social Risks Governance Hlt, Chongqing, Peoples R China.; Lei, X (通讯作者)，Chongqing Med Univ, Res Ctr Publ Hlth Secur, 1 Med Coll Rd, Chongqing 400016, Peoples R China.; Xu, XM (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Childrens Hosp,Key Lab Child Dev & Disorders, Big Data Ctr Childrens Med Care,Minist Educ, 136 Zhongshan 2nd Rd, Chongqing 400014, Peoples R China."

来源：BMC INFECTIOUS DISEASES

ESI学科分类：IMMUNOLOGY

WOS号：WOS:001125471400001

JCR分区：Q2

影响因子：3.4

年份：2023

卷号：23

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：EEMD; LSTM; Hybrid model; HFMD prediction

摘要："BackgroundHand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD.MethodsThe data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models.ResultsOverall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996.ConclusionResults showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series."

基金机构：National Key Research and Development Program of China; Children's Hospital of Chongqing Medical University

基金资助正文："We would like to thank Children's Hospital of Chongqing Medical University for providing the data of confirmed hand, foot and mouth disease cases in Chongqing."