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Stationary distribution, extinction and probability density function of a stochastic SEIV epidemic model with general incidence and Ornstein–Uhlenbeck process

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  • Su, Tan
  • Yang, Qing
  • Zhang, Xinhong
  • Jiang, Daqing

Abstract

Considering the great benefit of vaccination and the variability of environmental influence, a stochastic SEIV epidemic model with mean-reversion Ornstein–Uhlenbeck process and general incidence rate is investigated in this paper. First, it is theoretically proved that stochastic model has a unique global solution. Next, by constructing a series of suitable Lyapunov functions, we obtain a sufficient criterion R0s>1 for the existence of stationary distribution which means the disease will last for a long time. Then, the sufficient condition for the extinction of the infectious disease is also derived. Furthermore, an exact expression of probability density function near the quasi-endemic equilibrium is obtained by solving the corresponding four-dimensional matrix equation. Finally, some numerical simulations are carried out to illustrate the theoretical results.

Suggested Citation

  • Su, Tan & Yang, Qing & Zhang, Xinhong & Jiang, Daqing, 2023. "Stationary distribution, extinction and probability density function of a stochastic SEIV epidemic model with general incidence and Ornstein–Uhlenbeck process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    • Handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001607
    DOI: 10.1016/j.physa.2023.128605
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    References listed on IDEAS

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    1. Cai, Yongli & Jiao, Jianjun & Gui, Zhanji & Liu, Yuting & Wang, Weiming, 2018. "Environmental variability in a stochastic epidemic model," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 210-226.
    2. Zhang, Xiaofeng & Yuan, Rong, 2021. "A stochastic chemostat model with mean-reverting Ornstein-Uhlenbeck process and Monod-Haldane response function," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    3. Zhang, Shengqiang & Yuan, Sanling & Zhang, Tonghua, 2022. "A predator-prey model with different response functions to juvenile and adult prey in deterministic and stochastic environments," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    4. Ayoubi, Tawfiqullah & Bao, Haibo, 2020. "Persistence and extinction in stochastic delay Logistic equation by incorporating Ornstein-Uhlenbeck process," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    5. Zhang, Xinhong & Shi, Zhenfeng & Wang, Yuanyuan, 2019. "Dynamics of a stochastic avian–human influenza epidemic model with mutation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Shi, Zhenfeng & Zhang, Xinhong & Jiang, Daqing, 2019. "Dynamics of an avian influenza model with half-saturated incidence," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 399-416.
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    Cited by:

    1. Zhang, Ge & Li, Zhiming & Din, Anwarud & Chen, Tao, 2024. "Dynamic analysis and optimal control of a stochastic COVID-19 model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 498-517.
    2. Lili Kong & Luping Li & Shugui Kang & Fu Chen, 2023. "Dynamic Behavior of a Stochastic Avian Influenza Model with Two Strains of Zoonotic Virus," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

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