Back to Search
Start Over
A review on COVID-19 forecasting models
- 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.
- Subjects :
- 0209 industrial biotechnology
2019-20 coronavirus outbreak
Time series
Coronavirus disease 2019 (COVID-19)
S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems
Computer science
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
COVID-19
Outbreak
02 engineering and technology
Data science
020901 industrial engineering & automation
Work (electrical)
Section (archaeology)
Artificial Intelligence
SIR
0202 electrical engineering, electronic engineering, information engineering
Subject areas
Artificial Intelligence & Image Processing
020201 artificial intelligence & image processing
SEIR
Analysis
Software
Forecasting
0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects
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