A review of the potential of artificial intelligence approaches to forecasting COVID-19 spreading

Mohammad Behdad Jamshidi, Sobhan Roshani, Jakub Talla, Ali Lalbakhsh, Zdeněk Peroutka, Saeed Roshani, Fariborz Parandin, Zahra Malek*, Fatemeh Daneshfar, Hamid Reza Niazkar, Saeedeh Lotfi, Asal Sabet, Mojgan Dehghani, Farimah Hadjilooei, Maryam S. Sharifi-Atashgah, Pedram Lalbakhsh

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

14 Citations (Scopus)
25 Downloads (Pure)


The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors.
Original languageEnglish
Pages (from-to)493-511
Number of pages19
Issue number2
Publication statusPublished - Jun 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • artificial intelligence
  • COVID-19
  • deep learning
  • epidemiological disease
  • machine learning
  • LSTM
  • spreading
  • SARS-CoV-2


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