doi: 10.3934/dcdsb.2020335

Persistence and extinction of a stochastic SIS epidemic model with regime switching and Lévy jumps

School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei 430074, China

* Corresponding author: Shangjiang Guo

Received  March 2020 Revised  July 2020 Published  November 2020

Fund Project: The first author is supported by China Postdoctoral Science Foundation, and the Post-doctoral Innovative Research Positions in Hubei Province (Grant No. 1232037), the second author is supported by NSFC (Grant Nos. 11671123, 12071446)

This paper is devoted to a stochastic regime-switching susceptible-infected-susceptible epidemic model with nonlinear incidence rate and Lévy jumps. A threshold $ \lambda $ in terms of the invariant measure, different from the usual basic reproduction number, is obtained to completely determine the extinction and prevalence of the disease: if $ \lambda>0 $, the disease is persistent and there is a stationary distribution; if $ \lambda<0 $, the disease goes to extinction and the susceptible population converges weakly to a boundary distribution. Moreover, some numerical simulations are performed to illustrate our theoretical results. It is very interesting to notice that random fluctuations (including the white noise and Lévy noise) acting the infected individuals can prevent the outbreak of disease, that the disease of a regime-switching model may have the opportunity to persist eventually even if it is extinct in one regime, and that the prevalence of the disease can also be controlled by reducing the value of transmission rate of disease.

Citation: Shangzhi Li, Shangjiang Guo. Persistence and extinction of a stochastic SIS epidemic model with regime switching and Lévy jumps. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020335
References:
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J. BaoX. MaoG. Yin and C. Yuan, Competitive Lotka-Volterra population dynamics with jumps, Nonlinear Anal., 74 (2011), 6601-6616.   Google Scholar

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Y. Guo, Stochastic regime switching SIR model driven by Lévy noise, Physica A: Statistical Mechanics and its Applications, 479 (2017), 1-11.   Google Scholar

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H.J. Li and S. Guo, Dynamics of a SIRC epidemiological model, Electronic Journal of Differential Equations, 2017 (2017), Paper No. 121, 18 pp. Google Scholar

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H. Qiu and S. Guo, Steady-states of a Leslie-Gower model with diffusion and advection, Applied Mathematics and Computation, 346 (2019), 695-709.   Google Scholar

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H. Qiu, S. Guo and S. Li, Stability and bifurcation in a predator-prey system with prey-taxis, Int. J. Bifur. Chaos, 30 (2020), 2050022, 25pp. doi: 10.1142/S0218127420500224.  Google Scholar

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[30]

Z. Teng and L. Wang, Persistence and extinction for a class of stochastic SIS epidemic models with nonlinear incidence rate, Physic A, 451 (2016), 507-518.  doi: 10.1016/j.physa.2016.01.084.  Google Scholar

[31] K. Wang, Stochastic Biomathematics Models, Science Press, Beijing, 2010.   Google Scholar
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C. Xu, Global threshold dynamics of a stochastic differential equation SIS model, Journal of Mathematical Analysis and Applications, 447 (2017), 736-757.   Google Scholar

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G. G. Yin and C. Zhu, Hybrid Switching Diffusions: Properties and Applications, Stoch. Model. Appl. Probab. 63, Springer, New York, 2010. Google Scholar

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X. ZhongS. Guo and M. Peng, Stability of stochastic SIRS epidemic models with saturated incidence rates and delay, Stochastic Analysis and Applications, 35 (2017), 1-26.  doi: 10.1080/07362994.2016.1244644.  Google Scholar

[36]

J. Zhou and H. W. Hethcote, Populations size dependent incidence in models for diseases without immunity, J. Math. Biol., 32 (1994), 809-834.   Google Scholar

[37]

Y. ZhouS. Yuan and D. Zhao, Threshold behavior of a stochastic SIS model with Lévy jumps, Applied Mathematics and Computation, 275 (2016), 255-267.   Google Scholar

[38]

Y. Zhou and W. Zhang, Threshold of a stochastic SIR epidemic model with Lévy jumps, Physica A: Statistical Mechanics and its Applications, 446 (2016), 204-216.   Google Scholar

[39]

C. Zhu, Critical result on the threshold of a stochastic SIS model with saturated incidence rate, Physica A, 523 (2019), 426-437.  doi: 10.1016/j.physa.2019.02.012.  Google Scholar

[40]

R. Zou and S. Guo, Dynamics of a diffusive Leslie-Gower predator-prey model in spatially heterogeneous environment, Discrete & Continuous Dynamical Systems-B, 25 (2020), 4189-4210.  doi: 10.3934/dcdsb.2020093.  Google Scholar

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R. Zou and S. Guo, Dynamics in a diffusive predator-prey system with ratio-dependent predator influence, Computers and Mathematics with Applications, 75 (2018), 1237-1258.  doi: 10.1016/j.camwa.2017.11.002.  Google Scholar

show all references

References:
[1] D. Applebaum, Lévy Processes and Stochastic Calculus, 2nd edition, Cambridge University Press, 2009.   Google Scholar
[2]

J. BaoX. MaoG. Yin and C. Yuan, Competitive Lotka-Volterra population dynamics with jumps, Nonlinear Anal., 74 (2011), 6601-6616.   Google Scholar

[3]

J. Bao and J. Shao, Asymptotic behavior of SIRS models in state-dependent random environments, arXiv: 1802.02309. Google Scholar

[4]

J. Bao and C. Yuan, Stochastic population dynamics driven by Lévy noise, J. Math. Anal. Appl., 391 (2012), 363-375.   Google Scholar

[5]

I. Barbalat, Systemes déquations différentielles doscillations non linéaires, Rev. Math. Pures Appl., 4 (1959), 267-270.   Google Scholar

[6]

S. CaiY. Cai and X. Mao, A stochastic differential equation SIS epidemic model with two independent Brownian motions, Journal of Mathematical Analysis and Applications, 474 (2019), 1536-1550.  doi: 10.1016/j.jmaa.2019.02.039.  Google Scholar

[7]

M.-F. Chen, From Markov Chains to Non-equilibrium Particle Systems, 2nd ed., World Scientific, River Edge, NJ, 2004. Google Scholar

[8]

O. DiekmannJ. A. P. Heesterbeek and J. A. Metz, On the definition and the computation of the basic reproduction ratio in models for infectious diseases in heterogeneous populations, Journal of Mathematical Biology, 28 (1990), 365-382.   Google Scholar

[9]

N. H. DuR. KonK. Sato and Y. Takeuchi, Dynamical behaviour of Lotka-Volterra competition systems: Nonautonomous bistable case and the effect of telegraph noise, J. Comput. Appl. Math., 170 (2004), 399-422.   Google Scholar

[10]

J. Gao and S. Guo, Effect of prey-taxis and diffusion on positive steady states for a predator-prey system, Math Meth Appl Sci., 41 (2018), 3570-3587.  doi: 10.1002/mma.4847.  Google Scholar

[11]

J. Gao and S. Guo, Patterns in a modified Leslie-Gower model with Beddington-DeAngelis functional response and nonlocal prey competition, Internat. J. Bifur. Chaos Appl. Sci. Engrg., 30 (2020), 2050074, 28pp. doi: 10.1142/S0218127420500741.  Google Scholar

[12]

Q. GeG. JiJ. Xu and et al., Extinction and persistence of a stochastic nonlinear SIS epidemic model with jumps, Physica A: Statistical Mechanics and its Applications, 462 (2016), 1120-1127.   Google Scholar

[13]

A. GrayD. GreenhalghL. HuX. Mao and J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876-902.  doi: 10.1137/10081856X.  Google Scholar

[14]

A. GrayD. GreenhalghX. Mao and J. Pan, The SIS epidemic model with Markovian switchiing, J. Math. Anal. Appl., 394 (2012), 496-516.  doi: 10.1016/j.jmaa.2012.05.029.  Google Scholar

[15]

S. Guo, Bifurcation and spatio-temporal patterns in a diffusive predator-prey system, Nonlinear Analysis: Real World Applications, 42 (2018), 448-477.   Google Scholar

[16]

Y. Guo, Stochastic regime switching SIR model driven by Lévy noise, Physica A: Statistical Mechanics and its Applications, 479 (2017), 1-11.   Google Scholar

[17]

H.J. Li and S. Guo, Dynamics of a SIRC epidemiological model, Electronic Journal of Differential Equations, 2017 (2017), Paper No. 121, 18 pp. Google Scholar

[18]

S. Li and S. Guo, Permanence and extinction of a stochastic SIS epidemic model with three independent Brownian motions, Discrete & Continuous Dynamical Systems-B, 2020. doi: 10.3934/dcdsb.2020201.  Google Scholar

[19]

S. Li and S. Guo, Random attractors for stochastic semilinear degenerate parabolic equations with delay, Physica A, 550 (2020), 124164, 24pp. doi: 10.1016/j.physa.2020.124164.  Google Scholar

[20]

Y. Lin and Y. Zhao, Exponential ergodicity of a regime-switching SIS epidemic model with jumps, Applied Mathematics Letters, 94 (2019), 133-139.   Google Scholar

[21]

Q. Liu, The threshold of a stochastic Susceptible-Infective epidemic model under regime switching, Nonlinear Analysis: Hybrid Systems, 21 (2016), 49-58.   Google Scholar

[22]

Q. LiuD. JiangT. Hayat and A. Alsaedi, Dynamical behavior of a hybrid switching SIS epidemic model with vaccination and Lévy jumps, Stochastic Analysis and Applications, 37 (2019), 388-411.   Google Scholar

[23]

X. MaoG. Marion and E. Renshaw, Environmental noise suppresses explosion in population dynamics, Stochastic Process. Appl., 97 (2002), 95-110.   Google Scholar

[24]

D. H. Nguyen and G. Yin, Coexistence and exclusion of stochastic competitive Lotka-Volterra models, J. Differential Equations, 262 (2017), 1192-1225.  doi: 10.1016/j.jde.2016.10.005.  Google Scholar

[25]

H. Qiu and S. Guo, Steady-states of a Leslie-Gower model with diffusion and advection, Applied Mathematics and Computation, 346 (2019), 695-709.   Google Scholar

[26]

H. Qiu, S. Guo and S. Li, Stability and bifurcation in a predator-prey system with prey-taxis, Int. J. Bifur. Chaos, 30 (2020), 2050022, 25pp. doi: 10.1142/S0218127420500224.  Google Scholar

[27]

R. Situ, Theory of Stochastic Differential Equations with Jumps and Applications: Mathematical and Analytical Techniques with Applications to Engineering, Springer, 2005. Google Scholar

[28]

M. Slatkin, The dynamics of a population in a Markovian environment, Ecology, 59 (1978), 249-256.   Google Scholar

[29]

B. Sounvoravong, S. Guo and Y. Bai, Bifurcation and stability of a diffusive SIRS epidemic model with time delay, Electronic Journal of Differential Equations, 2019 (2019), Paper No. 45, 16 pp.  Google Scholar

[30]

Z. Teng and L. Wang, Persistence and extinction for a class of stochastic SIS epidemic models with nonlinear incidence rate, Physic A, 451 (2016), 507-518.  doi: 10.1016/j.physa.2016.01.084.  Google Scholar

[31] K. Wang, Stochastic Biomathematics Models, Science Press, Beijing, 2010.   Google Scholar
[32]

C. Xu, Global threshold dynamics of a stochastic differential equation SIS model, Journal of Mathematical Analysis and Applications, 447 (2017), 736-757.   Google Scholar

[33]

G. G. Yin and C. Zhu, Hybrid Switching Diffusions: Properties and Applications, Stoch. Model. Appl. Probab. 63, Springer, New York, 2010. Google Scholar

[34]

X. Zhang and K. Wang, Stochastic SIR model with jumps, Appl. Math. Lett., 26 (2013), 867-874.   Google Scholar

[35]

X. ZhongS. Guo and M. Peng, Stability of stochastic SIRS epidemic models with saturated incidence rates and delay, Stochastic Analysis and Applications, 35 (2017), 1-26.  doi: 10.1080/07362994.2016.1244644.  Google Scholar

[36]

J. Zhou and H. W. Hethcote, Populations size dependent incidence in models for diseases without immunity, J. Math. Biol., 32 (1994), 809-834.   Google Scholar

[37]

Y. ZhouS. Yuan and D. Zhao, Threshold behavior of a stochastic SIS model with Lévy jumps, Applied Mathematics and Computation, 275 (2016), 255-267.   Google Scholar

[38]

Y. Zhou and W. Zhang, Threshold of a stochastic SIR epidemic model with Lévy jumps, Physica A: Statistical Mechanics and its Applications, 446 (2016), 204-216.   Google Scholar

[39]

C. Zhu, Critical result on the threshold of a stochastic SIS model with saturated incidence rate, Physica A, 523 (2019), 426-437.  doi: 10.1016/j.physa.2019.02.012.  Google Scholar

[40]

R. Zou and S. Guo, Dynamics of a diffusive Leslie-Gower predator-prey model in spatially heterogeneous environment, Discrete & Continuous Dynamical Systems-B, 25 (2020), 4189-4210.  doi: 10.3934/dcdsb.2020093.  Google Scholar

[41]

R. Zou and S. Guo, Dynamics in a diffusive predator-prey system with ratio-dependent predator influence, Computers and Mathematics with Applications, 75 (2018), 1237-1258.  doi: 10.1016/j.camwa.2017.11.002.  Google Scholar

Figure 1.  Trajectories of solutions to model (2) with parameters (29) in regimes 1 and 2
Figure 2.  Trajectories of susceptible and infected populations of model (2) with parameters (29)
Figure 3.  The joint density distribution of $ (S,I) $ of model (2) with parameters (29). (a) The case without jumps; (b) The case with jumps
Figure 4.  Trajectories of solutions to model (2) with parameters (30) in regimes 1 and 2
Figure 5.  Trajectories of susceptible and infected populations of model (2) with parameters (30)
Figure 6.  The weak convergence of $ S $ to the stationary solution $ \varphi $ of (10) with parameters (30)
Figure 7.  The joint density distribution of $ (S,I) $ of model (2) with parameters (30). (a) The case without jumps; (b) The case with jumps
Figure 8.  Trajectories of solutions to model (2) with parameters (31) in regimes 1 and 2
Figure 9.  Trajectories of susceptible and infected populations of model (2) with parameters (31)
Figure 10.  The weak convergence of $ S $ to the stationary solution $ \varphi $ of (10) with parameters (31)
Figure 11.  The joint density distribution of $ (S,I) $ of model (2) with parameters (31). (a) The case without jumps; (b) The case with jumps
Figure 12.  Trajectories of solutions to model (2) with parameters (32) in regimes 1 and 2
Figure 13.  Trajectories of susceptible and infected populations of model (2) with parameters (32)
Figure 14.  The joint density distribution of $ (S,I) $ of model (2) with parameters (32). (a) The case without jumps; (b) The case with jumps
Figure 15.  Trajectories of solutions to model (2) with parameters (33) in regimes 1 and 2
Figure 16.  Trajectories of susceptible and infected populations of model (2) with parameters (33)
Figure 17.  The joint density distribution of $ (S,I) $ of model (2) with parameters (33). (a) The case without jumps; (b) The case with jumps
Figure 18.  Trajectories of solutions to model (2) with parameters (34) in regimes 1 and 2
Figure 19.  Trajectories of susceptible and infected populations of model (2) with parameters (34) in regimes 1 and 2
Figure 20.  The weak convergence of $ S $ to the stationary solution $ \varphi $ of (10) with parameters (34)
Figure 21.  The joint density distribution of $ (S,I) $ of model (2) with parameters (34). (a) The case without jumps; (b) The case with jumps
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