A review on COVID-19 forecasting models.
Neural Comput Appl
; : 1-11, 2021 Feb 04.
Article
in English
| MEDLINE | ID: covidwho-1074422
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
Language:
English
Journal:
Neural Comput Appl
Year:
2021
Document Type:
Article
Affiliation country:
S00521-020-05626-8
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