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Modeling COVID-19 transmission dynamics incorporating media coverage and vaccination.
Wang, Xiaojing; Liang, Yu; Li, Jiahui; Liu, Maoxing.
  • Wang X; School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
  • Liang Y; School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
  • Li J; School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
  • Liu M; School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Math Biosci Eng ; 20(6): 10392-10403, 2023 Apr 06.
Article in English | MEDLINE | ID: covidwho-2305521
ABSTRACT
The COVID-19 pandemic has caused widespread concern around the world. In order to study the impact of media coverage and vaccination on the spread of COVID-19, we establish an SVEAIQR infectious disease model, and fit the important parameters such as transmission rate, isolation rate and vaccine efficiency based on the data from Shanghai Municipal Health Commission and the National Health Commission of the People's Republic of China. Meanwhile, the control reproduction number and the final size are derived. Moreover, through sensitivity analysis by PRCC (partial rank correlation coefficient), we discuss the effects of both the behavior change constant $ k $ according to media coverage and the vaccine efficiency $ \varepsilon $ on the transmission of COVID-19. Numerical explorations of the model suggest that during the outbreak of the epidemic, media coverage can reduce the final size by about 0.26 times. Besides that, comparing with $ 50\% $ vaccine efficiency, when the vaccine efficiency reaches $ 90\% $, the peak value of infected people decreases by about 0.07 times. In addition, we simulate the impact of media coverage on the number of infected people in the case of vaccination or non-vaccination. Accordingly, the management departments should pay attention to the impact of vaccination and media coverage.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study Topics: Vaccines Limits: Humans Country/Region as subject: Asia Language: English Journal: Math Biosci Eng Year: 2023 Document Type: Article Affiliation country: Mbe.2023456

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study Topics: Vaccines Limits: Humans Country/Region as subject: Asia Language: English Journal: Math Biosci Eng Year: 2023 Document Type: Article Affiliation country: Mbe.2023456