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The heterogeneous mixing model of COVID-19 with interventions.
Duan, Moran; Jin, Zhen.
Afiliación
  • Duan M; School of Data Science and Technology, North University of China, Taiyuan 030051, Shanxi, China; Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.
  • Jin Z; Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China; Shanxi Key Laboratory of Mathematical Technique and Big Data Analysis on Disease Control and Prevention, Taiyuan 030006, Shanxi, China. Electronic address: jinzhn@263.net.
J Theor Biol ; 553: 111258, 2022 11 21.
Article en En | MEDLINE | ID: mdl-36041504
ABSTRACT
The emergence of mutant strains of COVID-19 reduces the effectiveness of vaccines in preventing infection, but remains effective in preventing severe illness and death. This paper established a heterogeneous mixing model of age groups with pharmaceutical and non-pharmaceutical interventions by analyzing the transmission mechanism of breakthrough infection caused by the heterogeneity of protection period under the action of vaccine-preventable infection with the original strain. The control reproduction number Rc of the system is analyzed, and the existence and stability of equilibrium are given by the comparison principle. Numerical simulation was conducted to evaluate the vaccination program and intervention measures in the customized scenario, demonstrating that the group-3 coverage rate p3 plays a key role in Rc. It is proposed that accelerating the rate of admission and testing is conducive to epidemic control by further fitting data of COVID-19 transmission in real scenarios. The findings provide a general modeling idea for the emergence of new vaccines to prevent infection by mutant strains, as well as a solid theoretical foundation for mainland China to formulate future vaccination strategies for new vaccines. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Vacunas / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Theor Biol Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Vacunas / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Theor Biol Año: 2022 Tipo del documento: Article País de afiliación: China