A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning

作者全名："Liu, Shidi; Wan, Yiran; Yang, Wen; Tan, Andi; Jian, Jinfeng; Lei, Xun"

作者地址："[Liu, Shidi; Wan, Yiran; Yang, Wen; Jian, Jinfeng; Lei, Xun] Chongqing Med Univ, Sch Publ Hlth & Management, Chongqing 400016, Peoples R China; [Liu, Shidi; Wan, Yiran; Yang, Wen; Jian, Jinfeng; Lei, Xun] Chongqing Med Univ, Res Ctr Med & Social Dev, Chongqing 400016, Peoples R China; [Liu, Shidi; Wan, Yiran; Yang, Wen; Jian, Jinfeng; Lei, Xun] Chongqing Med Univ, Collaborat Innovat Ctr Social Risks Governance Hlt, Chongqing 400016, Peoples R China; [Liu, Shidi; Wan, Yiran; Yang, Wen; Jian, Jinfeng; Lei, Xun] Chongqing Med Univ, Res Ctr Publ Hlth Secur, Chongqing 400016, Peoples R China; [Tan, Andi] Yunnan Univ Finance & Econ, Int Business Sch, 237,Longquan Rd, Kunming 650221, Peoples R China"

通信作者："Lei, X (通讯作者)，Chongqing Med Univ, Sch Publ Hlth & Management, Chongqing 400016, Peoples R China.; Lei, X (通讯作者)，Chongqing Med Univ, Res Ctr Med & Social Dev, Chongqing 400016, Peoples R China.; Lei, X (通讯作者)，Chongqing Med Univ, Collaborat Innovat Ctr Social Risks Governance Hlt, Chongqing 400016, Peoples R China.; Lei, X (通讯作者)，Chongqing Med Univ, Res Ctr Publ Hlth Secur, Chongqing 400016, Peoples R China."

来源：INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH

ESI学科分类：ENVIRONMENT/ECOLOGY

WOS号：WOS:000909288200001

JCR分区：Q2

影响因子：4.614

年份：2023

卷号：20

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：COVID-19 prediction; deep learning; long short-term memory; ensemble empirical mode decomposition

摘要："Background: The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 trends; Methods: The data were collected from the Harvard Dataverse. Epidemic data from 21 January 2020 to 25 April 2021 for California, the most severely affected state in the United States, were used to develop an LSTM model and an EEMD-LSTM hybrid model, which is an LSTM model combined with ensemble empirical mode decomposition. In this study, ninety percent of the data were adopted to fit the models as a training set, while the subsequent 10% were used to test the prediction effect of the models. The mean absolute percentage error, mean absolute error, and root mean square error were used to evaluate the prediction performances of the models; Results: The results indicated that the number of confirmed cases in California was increasing as of 25 April 2021, with no obvious evidence of a sharp decline. On 25 April 2021, the LSTM model predicted 3666418 confirmed cases, whereas the EEMD-LSTM predicted 3681150. The mean absolute percentage errors for the LSTM and EEMD-LSTM models were 0.0151 and 0.0051, respectively. The mean absolute and root mean square errors were 5.58 x 10(4) and 5.63 x 10(4) for the LSTM model and 1.9 x 10(4) and 2.43 x 10(4) for the EEMD-LSTM model, respectively; Conclusions: The results showed the advantage of an EEMD-LSTM model over a single LSTM model, and the established EEMD-LSTM model may be suitable for monitoring and evaluating the epidemic situation and providing quantitative analysis evidence for epidemic prevention and control."

基金机构：Chongqing Science and Technology Program; [cstc2020jscx-cylhX0003]

基金资助正文：This work was funded by the Chongqing Science and Technology Program (Grant No. cstc2020jcyj-msxmX0279) and the Chongqing Science and Technology Program (Grant No. cstc2020jscx-cylhX0003).