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Prediction of the COVID-19 epidemic trends based on SEIR and AI models.

Authors
  • Feng, Shuo1
  • Feng, Zebang1
  • Ling, Chen2
  • Chang, Chen3
  • Feng, Zhongke3
  • 1 School of Software and Microelectronics, Peking University, Beijing, China. , (China)
  • 2 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China. , (China)
  • 3 Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing, China. , (China)
Type
Published Article
Journal
PLoS ONE
Publisher
Public Library of Science
Publication Date
Jan 01, 2021
Volume
16
Issue
1
Identifiers
DOI: 10.1371/journal.pone.0245101
PMID: 33417605
Source
Medline
Language
English
License
Unknown

Abstract

In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.

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