Global stability of multi-group SEIQR epidemic models with stochastic perturbation in computer network

Ramziya Rifhat; Kai Wang; Lei Wang; Ting Zeng; Zhidong Teng; Ramziya Rifhat; Kai Wang; Lei Wang; Ting Zeng; Zhidong Teng

doi:10.3934/era.2023212

Electronic Research Archive

2023, Volume 31, Issue 7: 4155-4184. doi: 10.3934/era.2023212

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Research article

Global stability of multi-group SEIQR epidemic models with stochastic perturbation in computer network

College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China

Received: 06 February 2023 Revised: 03 April 2023 Accepted: 24 April 2023 Published: 29 May 2023

In this paper, a class of multi-group SEIQR models with random perturbation in computer network is investigated. The existence and uniqueness of global positive solution with any positive initial value are obtained. The sufficient conditions on the asymptotic behavior of solutions around the disease-free equilibrium and endemic equilibrium of the corresponding deterministic model are established. Furthermore, the existence and uniqueness of stationary distribution are also obtained. Lastly, the analytical results are illustrated by the numerical simulations.
- computer network,
- multi-group stochastical SEIQR epidemic model,
- asymptotic behavior,
- lyapunov function,
- numerical simulation
Citation: Ramziya Rifhat, Kai Wang, Lei Wang, Ting Zeng, Zhidong Teng. Global stability of multi-group SEIQR epidemic models with stochastic perturbation in computer network[J]. Electronic Research Archive, 2023, 31(7): 4155-4184. doi: 10.3934/era.2023212

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Abstract

In this paper, a class of multi-group SEIQR models with random perturbation in computer network is investigated. The existence and uniqueness of global positive solution with any positive initial value are obtained. The sufficient conditions on the asymptotic behavior of solutions around the disease-free equilibrium and endemic equilibrium of the corresponding deterministic model are established. Furthermore, the existence and uniqueness of stationary distribution are also obtained. Lastly, the analytical results are illustrated by the numerical simulations.

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