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Epidemic threshold of a COVID-19 model with gaussian white noise and semi-Markov switching

In this study, we investigate the COVID-19 propagation dynamics using a stochastic SIQR model with Gaussian white noise and semi-Markovian switching, focusing on the impacts of Gaussian white noise and semi-Markovian switching on the propagation dynamics of COVID-19. It is suggested that the fate of...

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Detalles Bibliográficos
Autores principales: Sun, Qianqian, Tan, Dejun, Zhang, Shuwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Franklin Institute. Published by Elsevier Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152832/
https://www.ncbi.nlm.nih.gov/pubmed/37251516
http://dx.doi.org/10.1016/j.jfranklin.2023.04.035
Descripción
Sumario:In this study, we investigate the COVID-19 propagation dynamics using a stochastic SIQR model with Gaussian white noise and semi-Markovian switching, focusing on the impacts of Gaussian white noise and semi-Markovian switching on the propagation dynamics of COVID-19. It is suggested that the fate of COVID-19 is entirely determined by the basic reproduction number [Formula: see text] , under mild extra conditions. By making sensitivity analysis on [Formula: see text] , we found that the effect of quarantine rate on [Formula: see text] was more significant compared to transmission rate. Our results demonstrate that: (i) The presence of Gaussian white noise, while reducing the basic reproduction number [Formula: see text] of COVID-19, also poses more challenges for the prediction and control of COVID-19 propagation. (ii) The conditional holding time distribution has a significant effect on the kinetics of COVID-19. (iii) The semi-Markov switching and Gaussian white noise can support irregular recurrence of COVID-19 outbreaks.