A review on COVID-19 forecasting models

被引:151
|
作者
Rahimi, Iman [1 ]
Chen, Fang [2 ]
Gandomi, Amir H. [2 ]
机构
[1] Univ Putra Malaysia, Fac Engn, Dept Mech & Mfg Engn, Seri Kembangan, Malaysia
[2] Univ Technol Sydney, Data Sci Inst, Ultimo, NSW 2007, Australia
来源
NEURAL COMPUTING & APPLICATIONS | 2021年 / 35卷 / 33期
关键词
Forecasting; Analysis; COVID-19; SIR; SEIR; Time series; NEURAL-NETWORK; SCALE MIXTURES; EPIDEMIC; SPREAD;
D O I
10.1007/s00521-020-05626-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:23671 / 23681
页数:11
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