IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0282621.html
   My bibliography  Save this article

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

Author

Listed:
  • Erick Giovani Sperandio Nascimento
  • Júnia Ortiz
  • Adhvan Novais Furtado
  • Diego Frias

Abstract

This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models’ performances both for the prediction of deaths and confirmed cases (p-value

Suggested Citation

  • Erick Giovani Sperandio Nascimento & Júnia Ortiz & Adhvan Novais Furtado & Diego Frias, 2023. "Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-34, April.
    • Handle: RePEc:plo:pone00:0282621
    DOI: 10.1371/journal.pone.0282621
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282621
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0282621&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0282621?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Comparison of deep learning approaches to predict COVID-19 infection," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Claudia Barría-Sandoval & Guillermo Ferreira & Katherine Benz-Parra & Pablo López-Flores, 2021. "Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    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. 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.
    2. 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).
    3. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. 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).
    5. Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    8. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    9. Shruti Sharma & Yogesh Kumar Gupta & Abhinava K. Mishra, 2023. "Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods," IJERPH, MDPI, vol. 20(11), pages 1-23, May.
    10. 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).
    11. Abu Reza Md. Towfiqul Islam & Md. Hasanuzzaman & Md. Abul Kalam Azad & Roquia Salam & Farzana Zannat Toshi & Md. Sanjid Islam Khan & G. M. Monirul Alam & Sobhy M. Ibrahim, 2021. "Effect of meteorological factors on COVID-19 cases in Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9139-9162, June.
    12. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    13. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    14. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    16. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    17. Ashwin Muniyappan & Balamuralitharan Sundarappan & Poongodi Manoharan & Mounir Hamdi & Kaamran Raahemifar & Sami Bourouis & Vijayakumar Varadarajan, 2022. "Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series Solutions by Using HPM," Mathematics, MDPI, vol. 10(3), pages 1-27, January.
    18. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    19. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    20. Faik Bilgili & Emrah Koçak & Sevda Kuşkaya, 2023. "Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model," Evaluation Review, , vol. 47(4), pages 630-652, August.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0282621. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.