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Stochastic Epidemic Model for COVID-19 Transmission under Intervention Strategies in China

Author

Listed:
  • Zin Thu Win

    (School of Mathematics, Harbin Institute of Technology, Harbin 150001, China)

  • Mahmoud A. Eissa

    (Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Menoufia 32511, Egypt)

  • Boping Tian

    (School of Mathematics, Harbin Institute of Technology, Harbin 150001, China)

Abstract

In this paper, we discuss an EIQJR model with stochastic perturbation. First, a globally positive solution of the proposed model has been discussed. In addition, the global asymptotic stability and exponential mean-square stability of the disease-free equilibrium have been proven under suitable conditions for our model. This means that the disease will die over time. We investigate the asymptotic behavior around the endemic equilibrium of the deterministic model to show when the disease will prevail. Constructing a suitable Lyapunov functional method is crucial to our investigation. Parameter estimations and numerical simulations are performed to depict the transmission process of COVID-19 pandemic in China and to support analytical results.

Suggested Citation

  • Zin Thu Win & Mahmoud A. Eissa & Boping Tian, 2022. "Stochastic Epidemic Model for COVID-19 Transmission under Intervention Strategies in China," Mathematics, MDPI, vol. 10(17), pages 1-17, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3119-:d:902416
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    References listed on IDEAS

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    2. Mahmoud A. Eissa & Haiying Zhang & Yu Xiao, 2018. "Mean-Square Stability of Split-Step Theta Milstein Methods for Stochastic Differential Equations," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, January.
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    5. Tornatore, Elisabetta & Maria Buccellato, Stefania & Vetro, Pasquale, 2005. "Stability of a stochastic SIR system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 111-126.
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    Cited by:

    1. Khan, Junaid Iqbal & Ullah, Farman & Lee, Sungchang, 2022. "Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

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