Research article Special Issues

Influence of spatial heterogeneous environment on long-term dynamics of a reaction-diffusion SVIR epidemic model with relaps

  • Received: 27 February 2019 Accepted: 27 May 2019 Published: 24 June 2019
  • In this paper by adding the factors of disease relapse and vaccination in the space hetero-geneous environment, we establish and discuss a class of reaction-diffusion SVIR model with relapse and a varying external source in spatial heterogeneous environment. By applying a different method than the Lyapunov function, we study the long-term dynamic behavior of this model by means of global exponential attractor theory and gradient flow method. The global asymptotic stability and the persistence of epidemic are proved. To test the validity of our theoretical results, we choose some specific epidemic disease with some more practical and more definitive official data to simulate the global stability and exponential attraction of the model. The simulation results showed that the factors of disease relapse, vaccination and spatial heterogeneity had a great influence on the persists uniformly of the disease.

    Citation: Cheng-Cheng Zhu, Jiang Zhu, Xiao-Lan Liu. Influence of spatial heterogeneous environment on long-term dynamics of a reaction-diffusion SVIR epidemic model with relaps[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5897-5922. doi: 10.3934/mbe.2019295

    Related Papers:

  • In this paper by adding the factors of disease relapse and vaccination in the space hetero-geneous environment, we establish and discuss a class of reaction-diffusion SVIR model with relapse and a varying external source in spatial heterogeneous environment. By applying a different method than the Lyapunov function, we study the long-term dynamic behavior of this model by means of global exponential attractor theory and gradient flow method. The global asymptotic stability and the persistence of epidemic are proved. To test the validity of our theoretical results, we choose some specific epidemic disease with some more practical and more definitive official data to simulate the global stability and exponential attraction of the model. The simulation results showed that the factors of disease relapse, vaccination and spatial heterogeneity had a great influence on the persists uniformly of the disease.


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    [1] F. Brauer and C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology (Second Edition), Springer, 2012.
    [2] K. Dietz, The first epidemic model: A historical note on P.D. En'ko, Australian J. Stat., 30 (1988), 56–65.
    [3] H. F. Huo and X. M. Zhang, Complex dynamics in an alcoholism model with the impact of Twitter, Math. Biosci., 281 (2016), 24–35.
    [4] H. F. Huo and C. C. Zhu, Influence of relapse in a giving up smoking model, Abstr. Appl. Anal., (2013), 525461.
    [5] H. F. Huo and X. M. Zhang, Modelling effects of treatment at home on tuberculosis transmission dynamics, Appl. Math. Model., 40 (2016), 9474–9484.
    [6] C. Vargas-De-León, On the global stability of SIS, SIR and SIRS epidemic models with standard incidence, Chaos Solitons Fractals, 44 (2011), 1106–1110.
    [7] C. C. Zhu and J. Zhu, Stability of a reaction-diffusion alcohol model with the impact of tax policy, Comput. Math. Appl., 74 (2017), 613–633.
    [8] Z. Hu, L. Chang, Z. Teng, et al., Bifurcation analysis of a discrete SIRS epidemic model with standard incidence rate, Adv. Differ. Equ., (2016) 2016, 155.
    [9] Z. Jiang, W. Ma and J. Wei, Global hopf bifurcation and permanence of a delayed SEIRS epidemic model, Math. Comput. Simul., 122 (2016), 35–54.
    [10] A. M. Yousef and S. M. Salman, Backward bifurcation in a fractional-order SIRS epidemic model with a nonlinear incidence rate, Int. J. Nonlinear Sci. Numer. Simul., 17 (2016), 401–412.
    [11] H. Zhao and M. Zhao, Global hopf bifurcation analysis of an susceptible-infective-removed epi-demic model incorporating media coverage with time delay, J. Biol. Dyn., 11 (2016), 8–24.
    [12] S. Whang, S. Choi and E. Jung, A dynamic model for tuberculosis transmission and optimal treatment strategies in South Korea, J. Theor. Biol., 279 (2011), 120–131.
    [13] S. Choi and E. Jung, Optimal Tuberculosis Prevention and Control Strategy from a Mathematical Model Based on Real Data, Bull. Math. Biol., 76 (2014), 1566–1589.
    [14] Y. Hosono and B. Ilyas, Travelling waves for a simple diffusive epidemic model, Math. Model Meth. Appl. Sci., 5 (1995), 935–966.
    [15] W. T. Li, G. Lin, C. Ma, et al., Traveling wave solutions of a nonlocal delayed SIR model without outbreak threshold, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014), 467–484.
    [16] R. Peng and X. Q. Zhao, A reaction–diffusion SIS epidemic model in a time-periodic environment, Nonlinearity, 25 (2012), 1451–1471.
    [17] X. Q. Zhao, Global dynamics of a reaction and diffusion model for Lyme disease, J. Math. Biol., 65 (2012), 787–808.
    [18] Y. X. Zhang and X. Q. Zhao, A reaction-diffusion Lyme disease model with seasonality, SIAM J. Appl. Math., 73 (2013), 2077–2099.
    [19] Y. J. Lou and X. Q. Zhao, A reaction–diffusion malaria model with incubation period in the vector population, J. Math. Biol., 62 (2011), 543–568.
    [20] G. Webb, A reaction–diffusion model for a deterministic diffusive models, J. Math. Anal. Appl., 84 (1981), 150–161.
    [21] W. E. Fitzgibbon, M. E. Parrott and G. F. Webb, Diffusion epidemic models with incubation and crisscross dynamics, Math. Biosci., 128 (1985), 131–155.
    [22] H. R. Thieme, Spectral bound and reproduction number for infinite-dimensional population struc-ture and time heterogeneity, SIAM J. Appl. Math., 70 (2009), 188–211.
    [23] W. Wang and X. Q. Zhao, A nonlocal and time-delayed reaction-diffusion model of dengue trans-mission, SIAM J. Appl. Math., 71 (2011), 147–168.
    [24] W. Wang and X. Q. Zhao, Basic reproduction numbers for reaction-diffusion epidemic models, SIAM J. Appl. Dyn. Syst., 11 (2012), 1652–1673.
    [25] Z. F. Xie, Cross-diffusion induced Turing instability for a three species food chain model, J. Math. Anal. Appl., 388 (2012), 539–547.
    [26] K. Hattaf and N. Yousfi, Global stability for reaction–diffusion equations in biology, Comput. Math. Appl., 67 (2014), 1439–1449.
    [27] E. Latosa and T. Suzuki, Global dynamics of a reaction–diffusion system with mass conservation, J. Math. Anal. Appl., 411 (2014), 107–118.
    [28] J. B. Wang, W. T. Li and F. Y. Yang, Traveling waves in a nonlocal dispersal SIR model with nonlocal delayed transmission, Commun. Nonlinear Sci. Numer. Simul., 27 (2015), 136–152.
    [29] Z. Xu, Traveling waves for a diffusive SEIR epidemic model, Comm. Pure Appl. Anal., 15(2016), 871–892.
    [30] Z. Xu and C. Ai, Traveling waves in a diffusive influenza epidemic model with vaccination, Appl. Math. Model., 40 (2016), 7265–7280.
    [31] C. C. Zhu, W. T. Li and F. Y. Yang, Traveling waves in a nonlocal dispersal SIRH model with relapse, Comput. Math. Appl., 73 (2017), 1707–1723.
    [32] C. C. Zhu, W. T. Li and F. Y. Yang, Traveling waves of a reaction-diffusion SIRQ epidemic model with relapse, J. Appl. Anal. Comput., 7 (2017), 147–171.
    [33] X. Bao, W. Shen and Z. Shen, Spreading speeds and traveling waves for space-time periodic nonlocal dispersal cooperative systems, Commun. Pure Appl. Anal., 18 (2019) 361–396.
    [34] K. Yamazaki and X. Wang, Global well-posedness and asymptotic behavior of solutions to a reaction-convection-diffusion cholera epidemic model, Discrete Contin. Dyn. Syst. Ser. B, 21 (2016), 1297–1316.
    [35] L. J. S. Allen, B. M. Bolker, Y. Lou, et al., Asymptotic profiles of the steady states for an SIS epidemic reaction-diffusion model, Discrete Contin. Dyn. Syst., 21 (2008), 1–20.
    [36] R. Peng, Asymptotic profiles of the positive steady state for an SIS epidemic reaction–diffusion model. Part I, J. Differ, Eq,, 247 (2009), 1096–1119.
    [37] R. Peng and S. Liu, Global stability of the steady states of an SIS epidemic reaction-diffusion model, Nonlinear Anal., 71 (2009), 239–247.
    [38] R. Peng and F. Yi, Asymptotic profile of the positive steady state for an SIS epidemic reaction–diffusion model: effects of epidemic risk and population movement, Phys. D, 259 (2013), 8–25.
    [39] T. Kuniya and J. Wang, Lyapunov functions and global stability for a spatially diffusive SIR epidemic model, Appl. Anal., (2016) http://dx.doi.org/10.1080/00036811.2016.1199796.
    [40] K. Deng and Y. Wu, Dynamics of a susceptible–infected–susceptible epidemic reaction–diffusion model, Proc. Roy. Soc. Edinburgh Sect. A, 146 (2016), 929–946.
    [41] Y. Wu and X. Zou, Asymptotic profiles of steady states for a diffusive SIS epidemic model with mass action infection mechanism, J. Differential Equations, 261 (2016), 4424–4447.
    [42] H. Li, R. Peng and F. B. Wang, Varying total population enhances disease persistence: Qualitative analysis on a diffusive SIS epidemic model, J. Differ. Equ., 262 (2017), 885–913.
    [43] Y. Cai, Y. Kang, M. Banerjeed, et al., Complex Dynamics of a host–parasite model with both horizontal and vertical transmissions in a spatial heterogeneous environment, Nonlinear Anal. Real World Appl., 40 (2018), 444–465.
    [44] Y. Tong and C. Lei, An SIS epidemic reaction–diffusion model with spontaneous infection in a spatially heterogeneous environment, Nonlinear Anal. Real World Appl., 41 (2018), 443–460.
    [45] R. Temam, Infinite-Dimensional Systems in Mechanics and Physics, Springer-Verlag, New York, 1997.
    [46] J. C. Robinson, Infinite-Dimensional Dynamical Systems: An Introduction to Dissipative Parabolic PDEs and the Theory of Global Attractors, Cambridge University Press, 2001.
    [47] M. Yang and C. Y. Sun, Exponential attractors for the strongly damped wave equations, Nonlinear Anal. Real World Appl., 11 (2010), 913–919.
    [48] T. Ma, The theory and method of partial differential equation, Science Press, Beijing, 2011.
    [49] T. Ma and S. Wang, Phase Transition Dynamics, Springer Science+Business Media, LLC 2014.
    [50] Y. Li, H. Wu and T. Zhao, Necessary and sufficient conditions for the existence of exponential attractors for semigroups, and applications, Nonlinear Anal., 75 (2012), 6297–6305.
    [51] Y. Zhong and C. K. Zhong, Exponential attractors for reaction-diffusion equations with arbitrary polynomial growth, Nonlinear Anal., 71(2009), 751–765.
    [52] J. Zhang and C. K. Zhong, The existence of global attractors for a class of reaction–diffusion equations with distribution derivatives terms in Rn, J. Math. Anal. Appl., 427 (2015), 365–376.
    [53] J. Zhang, P. E. Kloeden, M. Yang, et al., Global exponential κ-dissipative semigroups and expo-nential attraction, Discrete Contin. Dyn. Syst. Ser., 37(2017), 3487–3502.
    [54] J. Zhang, Y. Wang and C. K. Zhong, Robustness of exponentially κ-dissipative dynamical systems with perturbation, Discrete Contin. Dyn. Syst. Ser. B, 22 (2017), 3875–3890.
    [55] X. Wang and J. Wang, Analysis of cholera epidemics with bacterial growth and spatial movement, J. Biol. Dyn., 9 (2015), 233–261.
    [56] N. K. Vaidya, F. B. Wang and X. Zou, Avian in influenza dynamics in wild birds with bird mobility and spatial heterogeneous environment, Discrete Contin. Dyn. Syst. Ser. B, 17 (2012), 2829–2848.
    [57] K. Yamazaki, Global well-posedness of infectious disease models without life-time immunity: the cases of cholera and avian in influenza, Math. Med. Biol., 35 (2018), 428–445.
    [58] Q. X. Ye, Z. Y. Li, M. X. Wang, et al., Introduction to reaction–diffusion equations, second edition, Science Press, Beijing, 2011 (in Chinese).
    [59] H. L. Smith, Monotone dynamical systems, Mathematical Surveys and Monographs, vol.41, American Mathematical Society, Providence, RI, 1995.
    [60] I. I. Vrabie, C0 semigroups and application, Elsevier Science B.V., New York, 2003.
    [61] C. V. Pao, Nonlinear Parabolic and Elliptic Equations, New York, London: Plenum, 1992.
    [62] Z. Du and R. Peng, A priori L estimates for solutions of a class of reaction-diffusion systems, J. Math. Biol., 72 (2016), 1429–1439.
    [63] Global hepatitis report, 2017. Available from: http://www.who.int/hepatitis/publications/global-hepatitis-report2017/en/. 64. World Health Statistics, 2013. Available from: http://www.who.int/.
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