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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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 33; S. 23671 - 23681
Hauptverfasser:	Rahimi, Iman, Chen, Fang, Gandomi, Amir H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.11.2023 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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