IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p617-d1019538.html
   My bibliography  Save this article

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

Author

Listed:
  • Shidi Liu

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing 400016, China
    Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China)

  • Yiran Wan

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing 400016, China
    Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China)

  • Wen Yang

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing 400016, China
    Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China)

  • Andi Tan

    (International Business School, Yunnan University of Finance and Economics, No. 237, Longquan Road, Kunming 650221, China)

  • Jinfeng Jian

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing 400016, China
    Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China)

  • Xun Lei

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing 400016, China
    Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China)

Abstract

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 × 10 4 and 5.63 × 10 4 for the LSTM model and 1.9 × 10 4 and 2.43 × 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.

Suggested Citation

  • Shidi Liu & Yiran Wan & Wen Yang & Andi Tan & Jinfeng Jian & Xun Lei, 2022. "A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning," IJERPH, MDPI, vol. 20(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:617-:d:1019538
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/617/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/1/617/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    2. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    3. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    5. Wenchao Ban & Liangduo Shen, 2022. "PM2.5 Prediction Based on the CEEMDAN Algorithm and a Machine Learning Hybrid Model," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    6. Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
    8. Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
    9. Balduíno César Mateus & Mateus Mendes & José Torres Farinha & Rui Assis & António Marques Cardoso, 2021. "Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press," Energies, MDPI, vol. 14(21), pages 1-21, October.
    10. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    11. Naheliya, Bharti & Redhu, Poonam & Kumar, Kranti, 2024. "MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    12. Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    13. Yong Wang & Hui Wang & Xiaoqiang Guo & Xinhua Liu & Xiaowen Liu, 2022. "State Prediction Method for A-Class Insulation Board Production Line Based on Transfer Learning," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    14. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    15. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
    16. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    17. Xujian Zhao & Wei Li, 2021. "Trend Prediction of Event Popularity from Microblogs," Future Internet, MDPI, vol. 13(9), pages 1-13, August.
    18. Yong-Ju Jang & Min-Seung Kim & Chan-Ho Lee & Ji-Hye Choi & Jeong-Hee Lee & Sun-Hong Lee & Tae-Eung Sung, 2022. "A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-22, June.
    19. Shengwen Zhou & Shunsheng Guo & Baigang Du & Shuo Huang & Jun Guo, 2022. "A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    20. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:617-:d:1019538. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.