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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 33/2023

04.02.2021 | S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems

A review on COVID-19 forecasting models

verfasst von: Iman Rahimi, Fang Chen, Amir H. Gandomi

Erschienen in: Neural Computing and Applications | Ausgabe 33/2023

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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.
Vorheriger Artikel Deep neuro-fuzzy analytics for intelligent big data processing in smart ecosystems
Nächster Artikel A novel solution for finding postpartum haemorrhage using fuzzy neural techniques

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Metadaten
Titel
A review on COVID-19 forecasting models
verfasst von
Iman Rahimi
Fang Chen
Amir H. Gandomi
Publikationsdatum
04.02.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 33/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05626-8

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