Deep learning for Covid-19 forecasting: State-of-the-art review

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorRajab, Khairan
dc.contributor.authorCherukuri, Aswani Kumar
dc.contributor.authorElnagar, Ashraf
dc.contributor.authorSafaraliev, Murodbek
dc.date.accessioned2022-12-21T10:06:01Z
dc.date.available2022-12-21T10:06:01Z
dc.date.copyright© 2022
dc.date.issued2022-10-28
dc.description.abstractThe Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. © 2022 Elsevier B.V.
dc.identifier.citationKamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., & Safaraliev, M. (2022). Deep learning for covid-19 forecasting: State-of-the-art review. Neurocomputing, 511, 142-154. doi:10.1016/j.neucom.2022.09.005
dc.identifier.issn09252312
dc.identifier.urihttps://doi.org/10.1016/j.neucom.2022.09.005
dc.identifier.urihttps://hdl.handle.net/20.500.12519/728
dc.language.isoen_US
dc.publisherElsevier B.V.
dc.relationAuthors Affiliations : Kamalov, F., Canadian University Dubai, United Arab Emirates; Rajab, K., Najran University, Saudi Arabia; Cherukuri, A.K., Vellore Institute of Technology, India; Elnagar, A., University of Sharjah, United Arab Emirates; Safaraliev, M., Ural Federal University, Russian Federation
dc.relation.ispartofseriesNeurocomputing; Volume 511
dc.rightsLicense to reuse the abstract has been secured from Elsevier and Copyright Clearance Center.
dc.subjectCNN
dc.subjectCovid-19
dc.subjectDeep learning
dc.subjectForecasting
dc.subjectGNN
dc.subjectLSTM
dc.subjectMLP
dc.subjectSurvey
dc.titleDeep learning for Covid-19 forecasting: State-of-the-art review
dc.typeArticle

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