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An intelligent forecast for COVID‐19 based on single and multiple features.

Authors :
Wang, Yilei
Zhang, Yiting
Zhang, Xiujuan
Liang, Hai
Li, Guangshun
Wang, Xiaoying
Source :
International Journal of Intelligent Systems; Nov2022, Vol. 37 Issue 11, p9339-9356, 18p
Publication Year :
2022

Abstract

It is urgent to identify the development of the Corona Virus Disease 2019 (COVID‐19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID‐19. In this paper, we visually analyze the real‐time data of COVID‐19, to monitor the trend of COVID‐19 in the form of charts. At present, the COVID‐19 is still spreading. However, in the existing works, the visualization of COVID‐19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID‐19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all‐round way, we also predict the development trend of the COVID‐19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID‐19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID‐19. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08848173
Volume :
37
Issue :
11
Database :
Complementary Index
Journal :
International Journal of Intelligent Systems
Publication Type :
Academic Journal
Accession number :
159361784
Full Text :
https://doi.org/10.1002/int.22995