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A review on COVID-19 forecasting models

Authors :
Iman Rahimi
Amir H. Gandomi
Fang Chen
Source :
Neural Computing and Applications, Neural Computing & Applications
Publication Year :
2021
Publisher :
Springer Science and Business Media LLC, 2021.

Abstract

The Novel coronavirus (COVID-19) has distributed to more than 200 territory worldwide leading to about 24 million confirmed cases as of August 25, 2020. Several models have been released that forecast the outbreak globally. This work presents a review of the most important forecasting models against COVID-19 and shows a short analysis of each one. The work presented in this study possesses two parts. A detailed scientometric analysis was done in the first section that provides an influential tool for describing bibliometric analyses. The analysis was performed on data corresponding to COVID-19 using the Scopus and Web of Science databases. For analysis, keywords and subject areas were addressed while classification of forecasting models, criteria evaluation and comparison of solution approaches were done in the second section of the work. Conclusion and discussion are provided as the final sections of this study.

Details

Language :
English
ISSN :
14333058 and 09410643
Database :
OpenAIRE
Journal :
Neural Computing and Applications
Accession number :
edsair.doi.dedup.....87d4c6d6c0de9d3042513166294b79ba
Full Text :
https://doi.org/10.1007/s00521-020-05626-8