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    Comparative study of machine learning methods for COVID-19 transmission forecasting

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    Name:
    JBI-20-1316R2.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Dairi, Abdelkader
    Harrou, Fouzi cc
    Zeroual, Abdelhafid
    Hittawe, Mohamad
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2021-04-26
    Online Publication Date
    2021-04-26
    Print Publication Date
    2021-06
    Embargo End Date
    2022-04-01
    Submitted Date
    2020-10-11
    Permanent link to this record
    http://hdl.handle.net/10754/669035
    
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    Abstract
    Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.
    Citation
    Dairi, A., Harrou, F., Zeroual, A., Hittawe, M. M., & Sun, Y. (2021). Comparative study of machine learning methods for COVID-19 transmission forecasting. Journal of Biomedical Informatics, 103791. doi:10.1016/j.jbi.2021.103791
    Publisher
    Elsevier BV
    Journal
    Journal of Biomedical Informatics
    DOI
    10.1016/j.jbi.2021.103791
    PubMed ID
    33915272
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S1532046421001209
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jbi.2021.103791
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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