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Journal Article
A review on COVID-19 forecasting models
2023
Overview
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.
Publisher
Springer London,Springer Nature B.V
Subject
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ COVID-19
/ Data Mining and Knowledge Discovery
/ Image Processing and Computer Vision
/ Keywords
/ Probability and Statistics in Computer Science
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