A review on COVID-19 forecasting models

• Published in: Neural computing & applications Vol. 35; no. 33; pp. 23671 - 23681
• Main Authors: Rahimi, Iman, Chen, Fang, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bibliometrics; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Coronaviruses; COVID-19; Data Mining and Knowledge Discovery; Disease transmission; Epidemics; Forecasting; Image Processing and Computer Vision; Keywords; Machine learning; Mathematical models; Probability and Statistics in Computer Science; Quarantine; Research methodology; S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems; S.I: Deep Neuro-Fuzzy Analytics for Intelligent Big Data Processing in Smart Ecosystems; Viral diseases; Visualization; COVID-19; Forecasting; SIR; SEIR; Analysis; Time series
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05626-8

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.