Processing math: 100%
Research article

Dynamical analysis of a stochastic SIRS epidemic model with saturating contact rate

  • Received: 22 June 2020 Accepted: 24 August 2020 Published: 08 September 2020
  • In this paper, a stochastic SIRS epidemic model with saturating contact rate is constructed. First, for the deterministic system, the stability of the equilibria is discussed by using eigenvalue theory. Second, for the stochastic system, the threshold conditions of disease extinction and persistence are established. Our results indicate that a large environmental noise intensity can suppress the spread of disease. Conversely, if the intensity of environmental noise is small, the system has a stationary solution which indicates the disease is persistent. Eventually, we introduce some computer simulations to validate the theoretical results.

    Citation: Yang Chen, Wencai Zhao. Dynamical analysis of a stochastic SIRS epidemic model with saturating contact rate[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5925-5943. doi: 10.3934/mbe.2020316

    Related Papers:

    [1] Yuting Ding, Gaoyang Liu, Yong An . Stability and bifurcation analysis of a tumor-immune system with two delays and diffusion. Mathematical Biosciences and Engineering, 2022, 19(2): 1154-1173. doi: 10.3934/mbe.2022053
    [2] Shunyi Li . Hopf bifurcation, stability switches and chaos in a prey-predator system with three stage structure and two time delays. Mathematical Biosciences and Engineering, 2019, 16(6): 6934-6961. doi: 10.3934/mbe.2019348
    [3] Jianjun Paul Tian . The replicability of oncolytic virus: Defining conditions in tumor virotherapy. Mathematical Biosciences and Engineering, 2011, 8(3): 841-860. doi: 10.3934/mbe.2011.8.841
    [4] Qingfeng Tang, Guohong Zhang . Stability and Hopf bifurcations in a competitive tumour-immune system with intrinsic recruitment delay and chemotherapy. Mathematical Biosciences and Engineering, 2021, 18(3): 1941-1965. doi: 10.3934/mbe.2021101
    [5] LanJiang Luo, Haihong Liu, Fang Yan . Dynamic behavior of P53-Mdm2-Wip1 gene regulatory network under the influence of time delay and noise. Mathematical Biosciences and Engineering, 2023, 20(2): 2321-2347. doi: 10.3934/mbe.2023109
    [6] Suqi Ma . Low viral persistence of an immunological model. Mathematical Biosciences and Engineering, 2012, 9(4): 809-817. doi: 10.3934/mbe.2012.9.809
    [7] Hongying Shu, Wanxiao Xu, Zenghui Hao . Global dynamics of an immunosuppressive infection model with stage structure. Mathematical Biosciences and Engineering, 2020, 17(3): 2082-2102. doi: 10.3934/mbe.2020111
    [8] Hsiu-Chuan Wei . Mathematical modeling of tumor growth: the MCF-7 breast cancer cell line. Mathematical Biosciences and Engineering, 2019, 16(6): 6512-6535. doi: 10.3934/mbe.2019325
    [9] Juenu Yang, Fang Yan, Haihong Liu . Dynamic behavior of the p53-Mdm2 core module under the action of drug Nutlin and dual delays. Mathematical Biosciences and Engineering, 2021, 18(4): 3448-3468. doi: 10.3934/mbe.2021173
    [10] Yilong Li, Shigui Ruan, Dongmei Xiao . The Within-Host dynamics of malaria infection with immune response. Mathematical Biosciences and Engineering, 2011, 8(4): 999-1018. doi: 10.3934/mbe.2011.8.999
  • In this paper, a stochastic SIRS epidemic model with saturating contact rate is constructed. First, for the deterministic system, the stability of the equilibria is discussed by using eigenvalue theory. Second, for the stochastic system, the threshold conditions of disease extinction and persistence are established. Our results indicate that a large environmental noise intensity can suppress the spread of disease. Conversely, if the intensity of environmental noise is small, the system has a stationary solution which indicates the disease is persistent. Eventually, we introduce some computer simulations to validate the theoretical results.




    [1] S. Ullah, M. A. Khan, M. Farooq, T. Gul, Modeling and analysis of Tuberculosis (TB) in Khyber Pakhtunkhwa, Pakistan, Math. Comput. Simulation, 165 (2019), 181-199.
    [2] G. Sun, J. Xie, S. Huang, Z. Jin, M. Li, L. Liu, Transmission dynamics of cholera: Mathematical modeling and control strategies, Commun. Nonlinear Sci. Numer. Simul., 45 (2017), 235-244.
    [3] Y. Bai, X. Mu, Global asymptotic stability of a generalized SIRS epidemic model with transfer from infectious to susceptible, J. Appl. Anal. Comput., 8 (2018), 402-412.
    [4] J. Lai, S. Gao, Y. Liu, X. Meng, Impulsive switching epidemic model with benign worm defense and quarantine strategy, Complexity, 540 (2020), 1-12.
    [5] Q. Liu, D. Jiang, Threshold behavior in a stochastic SIR epidemic model with Logistic birth, Phys. A, 2020 (2020), 123488.
    [6] D. Zhao, S. Yuan, Threshold dynamics of the stochastic epidemic model with jump-diffusion infection force, J. Appl. Anal. Comput., 9 (2019), 440-451.
    [7] B. Zhang, Y. Cai, B. Wang, W. Wang, Dynamics and asymptotic profiles of steady states of an SIRS epidemic model in spatially heterogenous environment, Math. Biosci. Eng., 17 (2019), 893-909.
    [8] Y. Tu, S. Gao, Y. Liu, D. Chen, Y. Xu, Transmission dynamics and optimal control of stagestructured HLB model, Math. Biosci. Eng., 16 (2019), 5180.
    [9] H. W. Hethcote, Qualitative analyses of communicable disease models, Math. Biosci., 28 (1976), 335-356.
    [10] J. Chen, An SIRS epidemic model, Appl. Math. J. Chinese Univ. Ser. B, 19 (2004), 101-108.
    [11] T. Li, F. Zhang, H. Liu, Y. Chen, Threshold dynamics of an SIRS model with nonlinear incidence rate and transfer from infectious to susceptible, Appl. Math. Lett., 70 (2017), 52-57.
    [12] Y. Wang, G. Liu, Dynamics analysis of a stochastic SIRS epidemic model with nonlinear incidence rate and transfer from infectious to susceptible, Math. Biosci. Eng., 16 (2019), 6047-6070.
    [13] H. R. Thieme, C. Castillo-Chavez, On the role of variable infectivity in the dynamics of the human immunodeficiency virus epidemic, Mathematical and Statistical Approaches to AIDS Epidemiology, Springer, Berlin, 1989.
    [14] J. Heesterbeek, J. A. Metz, The saturating contact rate in marriage and epidemic models, J. Math. Biol., 31 (1993), 529-539.
    [15] H. Zhang, Y. Li, W. Xu, Global stability of an SEIS epidemic model with general saturation incidence, Appl. Math., 2013 (2013), 1-11.
    [16] G. Lan, Y. Huang, C. Wei, S. Zhang, A stochastic SIS epidemic model with saturating contact rate, Phys. A, 529 (2019), 121504.
    [17] Y. Cai, J. Jiao, Z. Gui, Y. Liu, W. Wang, Environmental variability in a stochastic epidemic model, Appl. Math. Comput., 329 (2018), 210-226.
    [18] D. Kiouach, L. Boulaasair, Stationary distribution and dynamic behaviour of a stochastic SIVR epidemic model with imperfect vaccine, J. Appl. Math., 2018 (2018), 1-11.
    [19] X. Zhang, H. Peng,, Stationary distribution of a stochastic cholera epidemic model with vaccination under regime switching, Appl. Math. Lett., 102 (2020), 106095.
    [20] W. Wang, C. Ji, Y. Bi, S.Liu, Stability and asymptoticity of stochastic epidemic model with interim immune class and independent perturbations, Appl. Math. Lett., 104 (2020), 106245.
    [21] R. Lu, F. Wei, Persistence and extinction for an age-structured stochastic SVIR epidemic model with generalized nonlinear incidence rate, Phys. A, 513 (2019), 572-587.
    [22] T. Feng, Z. Qiu, X. Meng, Dynamics of a stochastic hepatitis-c virus system with host immunity, Discrete Contin. Dyn. Syst. Ser. B, 24 (2019), 6367.
    [23] A. Gray, D. Greenhalgh, L. Hu, X. Mao, J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876-902.
    [24] W. Guo, Q. Zhang, X. Li, W. Wang, Dynamic behavior of a stochastic SIRS epidemic model with media coverage, Math. Methods Appl. Sci., 41 (2018), 5506-5525.
    [25] Y. Zhang, K. Fan, S. Gao, Y. Liu, S. Chen., Ergodic stationary distribution of a stochastic SIRS epidemic model incorporating media coverage and saturated incidence rate, Phys. A, 514 (2019), 671-685.
    [26] Y. Cai, Y. Kang, M. Banerjee, W. Wang, A stochastic SIRS epidemic model with infectious force under intervention strategies, J. Differ. Equations, 259 (2015), 7463-7502.
    [27] D. Jiang, Q. Liu, N. Shi, T. Hayat, A. Alsaedi, P. Xia, Dynamics of a stochastic HIV-1 infection model with logistic growth, Phys. A, 469 (2017), 706-717.
    [28] T. Feng, Z. Qiu, Global analysis of a stochastic TB model with vaccination and treatment, Discrete Contin. Dyn. Syst. Ser. B, 24 (2018), 2923.
    [29] S. Zhao, S. Yuan, H. Wang, Threshold behavior in a stochastic algal growth model with stoichiometric constraints and seasonal variation, J. Differ. Equations, 268 (2020), 5113-5139.
    [30] X. Ji, S. Yuan, T. Zhang, H. Zhu, Stochastic modeling of algal bloom dynamics with delayed nutrient recycling, Math. Biosci. Eng., 16 (2019), 1-24.
    [31] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, B. Ahmad, A stochastic SIRS epidemic model with logistic growth and general nonlinear incidence rate, Phys. A, 551 (2020), 124152.
    [32] S. Cai, Y. Cai, X. Mao, A stochastic differential equation SIS epidemic model with two correlated Brownian motions, Nonlinear Dynam., 97 (2019), 2175-2187.
    [33] Q. Liu, D. Jiang, N. Shi, T. Hayat, A. Alsaedi, The threshold of a stochastic SIS epidemic model with imperfect vaccination, Math. Comput. Simulation, 144 (2018), 78-90.
    [34] R. Khasminskii, Stochastic Stability of Differential Equations, Springer, Heidelberg, 2011.
    [35] D. Xu, Y. Huang, Z. Yang, Existence theorems for periodic Markov process and stochastic functional differential equations, Discrete Contin. Dyn. Syst., 24 (2009), 1005.
  • This article has been cited by:

    1. Yueping Dong, Rinko Miyazaki, Yasuhiro Takeuchi, Mathematical modeling on helper T cells in a tumor immune system, 2014, 19, 1553-524X, 55, 10.3934/dcdsb.2014.19.55
    2. F.S. Borges, K.C. Iarosz, H.P. Ren, A.M. Batista, M.S. Baptista, R.L. Viana, S.R. Lopes, C. Grebogi, Model for tumour growth with treatment by continuous and pulsed chemotherapy, 2014, 116, 03032647, 43, 10.1016/j.biosystems.2013.12.001
    3. Ping Bi, Shigui Ruan, Xinan Zhang, Periodic and chaotic oscillations in a tumor and immune system interaction model with three delays, 2014, 24, 1054-1500, 023101, 10.1063/1.4870363
    4. Ping Bi, Heying Xiao, Bifurcations of Tumor-Immune Competition Systems with Delay, 2014, 2014, 1085-3375, 1, 10.1155/2014/723159
    5. Liuyong Pang, Sanhong Liu, Xinan Zhang, Tianhai Tian, Mathematical modeling and dynamic analysis of anti-tumor immune response, 2020, 62, 1598-5865, 473, 10.1007/s12190-019-01292-9
    6. Zijian Liu, Jing Chen, Jianhua Pang, Ping Bi, Shigui Ruan, Modeling and Analysis of a Nonlinear Age-Structured Model for Tumor Cell Populations with Quiescence, 2018, 28, 0938-8974, 1763, 10.1007/s00332-018-9463-0
    7. Dipty Sharma, Paramjeet Singh, Discontinuous Galerkin method for a nonlinear age-structured tumor cell population model with proliferating and quiescent phases, 2021, 32, 0129-1831, 2150039, 10.1142/S012918312150039X
    8. Liuyong Pang, Shigui Ruan, Sanhong Liu, Zhong Zhao, Xinan Zhang, Transmission dynamics and optimal control of measles epidemics, 2015, 256, 00963003, 131, 10.1016/j.amc.2014.12.096
    9. Yaqin Shu, Jicai Huang, Yueping Dong, Yasuhiro Takeuchi, Mathematical modeling and bifurcation analysis of pro- and anti-tumor macrophages, 2020, 88, 0307904X, 758, 10.1016/j.apm.2020.06.042
    10. Léon Masurel, Carlo Bianca, Annie Lemarchand, Space-velocity thermostatted kinetic theory model of tumor growth, 2021, 18, 1551-0018, 5525, 10.3934/mbe.2021279
    11. Kaushik Dehingia, Parthasakha Das, Ranjit Kumar Upadhyay, Arvind Kumar Misra, Fathalla A. Rihan, Kamyar Hosseini, Modelling and analysis of delayed tumour–immune system with hunting T-cells, 2023, 203, 03784754, 669, 10.1016/j.matcom.2022.07.009
    12. Wei Li, Mengyang Li, Natasa Trisovic, Dynamical analysis of a kind of two-stage tumor-immune model under Gaussian white noises, 2023, 11, 2195-268X, 101, 10.1007/s40435-022-00959-9
    13. Jingnan Wang, Hongbin Shi, Li Xu, Lu Zang, Hopf bifurcation and chaos of tumor-Lymphatic model with two time delays, 2022, 157, 09600779, 111922, 10.1016/j.chaos.2022.111922
    14. Shujing Shi, Jicai Huang, Yang Kuang, Shigui Ruan, Stability and Hopf bifurcation of a tumor–immune system interaction model with an immune checkpoint inhibitor, 2023, 118, 10075704, 106996, 10.1016/j.cnsns.2022.106996
    15. Kaushik Dehingia, Hemanta Kumar Sarmah, Yamen Alharbi, Kamyar Hosseini, Mathematical analysis of a cancer model with time-delay in tumor-immune interaction and stimulation processes, 2021, 2021, 1687-1847, 10.1186/s13662-021-03621-4
    16. Gabriel Morgado, Annie Lemarchand, Carlo Bianca, From Cell–Cell Interaction to Stochastic and Deterministic Descriptions of a Cancer–Immune System Competition Model, 2023, 11, 2227-7390, 2188, 10.3390/math11092188
    17. Zhonghu Luo, Zijian Liu, Yuanshun Tan, Jin Yang, Modeling and analysis of a multilayer solid tumour with cell physiological age and resource limitations, 2024, 18, 1751-3758, 10.1080/17513758.2023.2295492
    18. Usman Pagalay, Sindi Ayuna Hustani, N.Z. Abd Hamid, W.-H. Chen, S. Side, W. Sanusi, M.A. Naufal, Dynamic Analysis of a Mathematical Model of the Anti-Tumor Immune Response, 2024, 58, 2271-2097, 01008, 10.1051/itmconf/20245801008
    19. Kaushik Dehingia, Yamen Alharbi, Vikas Pandey, A mathematical tumor growth model for exploring saturated response of M2 macrophages, 2024, 5, 27724425, 100306, 10.1016/j.health.2024.100306
    20. Carlo Bianca, A decade of thermostatted kinetic theory models for complex active matter living systems, 2024, 15710645, 10.1016/j.plrev.2024.06.015
    21. Syeda Alishwa Zanib, Muzamil Abbas Shah, A piecewise nonlinear fractional-order analysis of tumor dynamics: estrogen effects and sensitivity, 2024, 2363-6203, 10.1007/s40808-024-02094-0
    22. Ranjit Kumar Upadhyay, Amit Kumar Barman, Parthasakha Das, Binay Panda, On investigation of complexity in extracellular matrix-induced cancer dynamics under deterministic and stochastic framework, 2025, 0924-090X, 10.1007/s11071-024-10836-z
    23. Jianquan Li, Yuming Chen, Jiaojiao Guo, Huihui Wu, Xiaojian Xi, Dian Zhang, Dynamical Analysis of a Simple Tumor‐Immune Model With Two‐Stage Lymphocytes, 2025, 0170-4214, 10.1002/mma.10863
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4236) PDF downloads(93) Cited by(4)

Article outline

Figures and Tables

Figures(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog