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

Kamalov, Firuz
Rajab, Khairan
Cherukuri, Aswani Kumar
Elnagar, Ashraf
Safaraliev, Murodbek
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Elsevier B.V.
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.
This work is not available in the CUD collection. The version of the scholarly record of this work is published in Neurocomputing (2022), available online at:
CNN, Covid-19, Deep learning, Forecasting, GNN, LSTM, MLP, Survey
Kamalov, 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