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

Abstract

The 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.Dunarea de Jos” University of GalatiUmm Al-Qura Univer-sityUmm Al-Qura University, UQU, (22UQU4300274DSR01)Deanship of Scientific Research, King Saud UniversityFunding text 1: Deanship of Scientific Research at Umm Al-Qura University supported this work by Grant Code: (22UQU4300274DSR01).Funding text 2: Conceptualization, H.O.T., H.M.H.Z., A.M.A.M., G.A. and S.A.M.I.methodology, F.T.A. and H.O.T.software, D.S.B., A.H.A., H.M.H.Z. and A.E.validation, S.A.M.I., A.M.A.M., D.S.B., D.E.A., W.E., Y.S.R. and A.E.formal analysis, H.M.H.Z., and F.T.A.investigation, H.O.T., W.E., and G.A.resources, F.T.A. and D.S.B.data curation, S.A.M.I., A.H.A. and A.E.writing—original draft preparation, Y.S.R., D.E.A., H.O.T., D.E.A., F.T.A. and A.E.writing—review and editing, H.M.H.Z., S.I, A.M.A.M., A.H.A. and A.E.visualization, W.E. and A.E.supervision, H.M.H.Z., W.E., Y.S.R. and D.S.B.project administration, H.O.T., A.E., Y.S.R. and S.A.M.I.funding acquisition A.E. (The APC was funded by “Dunarea de Jos” University of Galati, Romania). All authors have read and agreed to the published version of the manuscript.Funding text 3: The authors would like to thank the Deanship of Scientific Research at the Umm Al-Qura Univer-sity for supporting this work by grant code (22UQU4300274DSR01). The APC was covered by “Dunarea de Jos” University of Galati, Romania