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Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments

In this article, we study the dynamical properties of susceptible-vaccinated-infected-susceptible (SVIS) epidemic system with saturated incidence rate and vaccination strategies. By constructing the suitable Lyapunov function, we examine the existence and uniqueness of the stochastic system. With th...

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Autores principales: Mahato, Prasenjit, Mahato, Sanat Kumar, Das, Subhashis, Karmakar, Partha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187527/
https://www.ncbi.nlm.nih.gov/pubmed/37191878
http://dx.doi.org/10.1007/s12064-023-00392-2
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author Mahato, Prasenjit
Mahato, Sanat Kumar
Das, Subhashis
Karmakar, Partha
author_facet Mahato, Prasenjit
Mahato, Sanat Kumar
Das, Subhashis
Karmakar, Partha
author_sort Mahato, Prasenjit
collection PubMed
description In this article, we study the dynamical properties of susceptible-vaccinated-infected-susceptible (SVIS) epidemic system with saturated incidence rate and vaccination strategies. By constructing the suitable Lyapunov function, we examine the existence and uniqueness of the stochastic system. With the help of Khas’minskii theory, we set up a critical value [Formula: see text] with respect to the basic reproduction number [Formula: see text] of the deterministic system. A unique ergodic stationary distribution is investigated under the condition of [Formula: see text] . In the epidemiological study, the ergodic stationary distribution represents that the disease will persist for long-term behavior. We focus for developing the general three-dimensional Fokker–Planck equation using appropriate solving theories. Around the quasi-endemic equilibrium, the probability density function of the stochastic system is analyzed which is the main theme of our study. Under [Formula: see text] , both the existence of ergodic stationary distribution and density function can elicit all the dynamical behavior of the disease persistence. The condition of disease extinction of the system is derived. For supporting theoretical study, we discuss the numerical results and the sensitivities of the biological parameters. Results and conclusions are highlighted.
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spelling pubmed-101875272023-05-17 Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments Mahato, Prasenjit Mahato, Sanat Kumar Das, Subhashis Karmakar, Partha Theory Biosci Original Article In this article, we study the dynamical properties of susceptible-vaccinated-infected-susceptible (SVIS) epidemic system with saturated incidence rate and vaccination strategies. By constructing the suitable Lyapunov function, we examine the existence and uniqueness of the stochastic system. With the help of Khas’minskii theory, we set up a critical value [Formula: see text] with respect to the basic reproduction number [Formula: see text] of the deterministic system. A unique ergodic stationary distribution is investigated under the condition of [Formula: see text] . In the epidemiological study, the ergodic stationary distribution represents that the disease will persist for long-term behavior. We focus for developing the general three-dimensional Fokker–Planck equation using appropriate solving theories. Around the quasi-endemic equilibrium, the probability density function of the stochastic system is analyzed which is the main theme of our study. Under [Formula: see text] , both the existence of ergodic stationary distribution and density function can elicit all the dynamical behavior of the disease persistence. The condition of disease extinction of the system is derived. For supporting theoretical study, we discuss the numerical results and the sensitivities of the biological parameters. Results and conclusions are highlighted. Springer Berlin Heidelberg 2023-05-16 2023 /pmc/articles/PMC10187527/ /pubmed/37191878 http://dx.doi.org/10.1007/s12064-023-00392-2 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Mahato, Prasenjit
Mahato, Sanat Kumar
Das, Subhashis
Karmakar, Partha
Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title_full Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title_fullStr Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title_full_unstemmed Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title_short Stationary distribution and density function analysis of SVIS epidemic model with saturated incidence and vaccination under stochastic environments
title_sort stationary distribution and density function analysis of svis epidemic model with saturated incidence and vaccination under stochastic environments
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187527/
https://www.ncbi.nlm.nih.gov/pubmed/37191878
http://dx.doi.org/10.1007/s12064-023-00392-2
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