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Review
. 2021 Feb 4:1-11.
doi: 10.1007/s00521-020-05626-8. Online ahead of print.

A review on COVID-19 forecasting models

Iman Rahimi  1 , Fang Chen  2 , Amir H Gandomi  2
Affiliations
Review

A review on COVID-19 forecasting models

Iman Rahimi et al. Neural Comput Appl. .

Abstract

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

Keywords: Analysis; COVID-19; Forecasting; SEIR; SIR; Time series.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Classification of scientific papers based on subject area
Fig. 2
Fig. 2
Research methodology used in this paper
Fig. 3
Fig. 3
Networks across the links (keywords analysis)
Fig. 4
Fig. 4
A detailed analysis (sum of works cited and number of records vs. Affiliations)
Fig. 5
Fig. 5
Susceptible, infected, and recovered (SIR) model
Fig. 6
Fig. 6
The susceptible, exposed, infected, and recovered (SEIR) diagram [18]
Fig. 7
Fig. 7
% of contribution of different solution approaches applied in the forecasting of COVID-19 confirmed cases
See this image and copyright information in PMC

References

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