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2019
DOI: 10.1155/2019/3575410
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Stationary Distribution and Extinction of a Stochastic SIQR Model with Saturated Incidence Rate

Abstract: In this paper, we consider a stochastic SIQR epidemic model with saturated incidence rate. By constructing a proper Lyapunov function, we obtain the existence and uniqueness of positive solution for this SIQR model. Furthermore, we study the dynamical properties of this stochastic SIQR model; that is, (i) we establish the sufficient condition for the existence of ergodic stationary distribution of the model; (ii) we obtain the extinction of the disease under some conditions. At last, numerical simulations are … Show more

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Cited by 10 publications
(6 citation statements)
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“…By using the tools of deterministic modeling, majority of the authors have explored the stability and optimal control strategies for epidemic models including a nonlinear incidence function [24][25][26][27][28][29][30][31][32][33][34]. For stochastic systems, many authors have investigated the stability analysis for different epidemic models including a nonlinear incidence function [35][36][37][38][39][40][41][42][43]. However, for the same class of models, a few papers addressed its optimal control theory [44][45][46].…”
Section: Introductionmentioning
confidence: 99%
“…By using the tools of deterministic modeling, majority of the authors have explored the stability and optimal control strategies for epidemic models including a nonlinear incidence function [24][25][26][27][28][29][30][31][32][33][34]. For stochastic systems, many authors have investigated the stability analysis for different epidemic models including a nonlinear incidence function [35][36][37][38][39][40][41][42][43]. However, for the same class of models, a few papers addressed its optimal control theory [44][45][46].…”
Section: Introductionmentioning
confidence: 99%
“…Remark Since the solutions of systems () and () represent populations, it is important to show positivity. Using Theorem 5 in Zhang and Zhou, 35 we can similarly obtain the positivity of the solution.…”
Section: Pa Methodsmentioning
confidence: 97%
“…Therefore, it is suitable to use the tools of stochastic modelling to describe such characteristics of infectious diseases, and perform better on local infectious diseases or a small number of infections, comparing to the deterministic model (3) . In this regard, various stochastic epidemic models were formulated and analyzed to reveal the influence of environmental noises [10] , [11] , [12] , [13] , [14] , [15] , [16] . El Fatini et al.…”
Section: Introductionmentioning
confidence: 99%