A review on COVID-19 forecasting models

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 analysi...

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Vydáno v:Neural computing & applications Ročník 35; číslo 33; s. 23671 - 23681
Hlavní autoři: Rahimi, Iman, Chen, Fang, Gandomi, Amir H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.11.2023
Springer Nature B.V
Témata:
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
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Popis
Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05626-8