Research article Special Issues

Dynamic modeling and analysis of Hepatitis B epidemic with general incidence


  • Received: 20 March 2023 Revised: 08 April 2023 Accepted: 14 April 2023 Published: 20 April 2023
  • New stochastic and deterministic Hepatitis B epidemic models with general incidence are established to study the dynamics of Hepatitis B virus (HBV) epidemic transmission. Optimal control strategies are developed to control the spread of HBV in the population. In this regard, we first calculate the basic reproduction number and the equilibrium points of the deterministic Hepatitis B model. And then the local asymptotic stability at the equilibrium point is studied. Secondly, the basic reproduction number of the stochastic Hepatitis B model is calculated. Appropriate Lyapunov functions are constructed, and the unique global positive solution of the stochastic model is verified by Itô formula. By applying a series of stochastic inequalities and strong number theorems, the moment exponential stability, the extinction and persistence of HBV at the equilibrium point are obtained. Finally, using the optimal control theory, the optimal control strategy to eliminate the spread of HBV is developed. To reduce Hepatitis B infection rates and to promote vaccination rates, three control variables are used, for instance, isolation of patients, treatment of patients, and vaccine inoculation. For the purpose of verifying the rationality of our main theoretical conclusions, the Runge-Kutta method is applied to numerical simulation.

    Citation: Tingting Xue, Long Zhang, Xiaolin Fan. Dynamic modeling and analysis of Hepatitis B epidemic with general incidence[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10883-10908. doi: 10.3934/mbe.2023483

    Related Papers:

  • New stochastic and deterministic Hepatitis B epidemic models with general incidence are established to study the dynamics of Hepatitis B virus (HBV) epidemic transmission. Optimal control strategies are developed to control the spread of HBV in the population. In this regard, we first calculate the basic reproduction number and the equilibrium points of the deterministic Hepatitis B model. And then the local asymptotic stability at the equilibrium point is studied. Secondly, the basic reproduction number of the stochastic Hepatitis B model is calculated. Appropriate Lyapunov functions are constructed, and the unique global positive solution of the stochastic model is verified by Itô formula. By applying a series of stochastic inequalities and strong number theorems, the moment exponential stability, the extinction and persistence of HBV at the equilibrium point are obtained. Finally, using the optimal control theory, the optimal control strategy to eliminate the spread of HBV is developed. To reduce Hepatitis B infection rates and to promote vaccination rates, three control variables are used, for instance, isolation of patients, treatment of patients, and vaccine inoculation. For the purpose of verifying the rationality of our main theoretical conclusions, the Runge-Kutta method is applied to numerical simulation.



    加载中


    [1] T. Khan, G. Zaman, M. I. Chohan, The transmission dynamic and optimal control of acute and chronic Hepatitis B, J. Biol. Dyn., 11 (2017), 172–189. https://doi.org/10.1080/17513758.2016.1256441 doi: 10.1080/17513758.2016.1256441
    [2] A. Din, Y. J. Li, Q. Liu, Viral dynamics and control of Hepatitis B virus (HBV) using an epidemic model, Alex. Eng. J., 59 (2020), 667–679. https://doi.org/10.1016/j.aej.2020.01.034 doi: 10.1016/j.aej.2020.01.034
    [3] T. Khan, Z. Ullah, Z. Ali, G. Zaman, Modeling and control of the Hepatitis B virus spreading using an epidemic model, Chaos, Solitons Fractals, 124 (2019), 1–9. https://doi.org/10.1016/j.chaos.2019.04.033 doi: 10.1016/j.chaos.2019.04.033
    [4] P. T. Karaji, N. Nyamoradi, Analysis of a fractional SIR model with general incidence function, Appl. Math. Lett., 108 (2020), 106499. https://doi.org/10.1016/j.aml.2020.106499 doi: 10.1016/j.aml.2020.106499
    [5] S. M. Simelane, P. G. Dlamini, A fractional order differential equation model for Hepatitis B virus with saturated incidence, Results Phys., 24 (2021), 104114. https://doi.org/10.1016/j.rinp.2021.104114 doi: 10.1016/j.rinp.2021.104114
    [6] F. Huang, J. L. Li, Exponential ultimate boundedness and stability of stochastic differential equations with impulese, Asian J. Control, 25 (2023), 88–100. https://doi.org/10.1002/asjc.2786 doi: 10.1002/asjc.2786
    [7] F. Huang, J. L. Li, Exponential ultimate boundedness and stability of impulsive stochastic functional differential equations, Int. J. Control, 96 (2023), 568–576. https://doi.org/10.1080/00207179.2021.2005259 doi: 10.1080/00207179.2021.2005259
    [8] T. Khan, A. Khan, G. Zaman, The extinction and persistence of the stochastic Hepatitis B epidemic model, Chaos, Solitons Fractals, 108 (2018), 123–128. https://doi.org/10.1016/j.chaos.2018.01.036 doi: 10.1016/j.chaos.2018.01.036
    [9] A. Din, Y. J. Li, T. Khan, K. Anwar, G. Zaman, Stochastic dynamics of Hepatitis B epidemics, Results Phys., 20 (2021), 103730. https://doi.org/10.1016/j.rinp.2020.103730 doi: 10.1016/j.rinp.2020.103730
    [10] P. J. Liu, A. Din, L. F. Huang, A. Yusuf, Stochastic optimal control analysis for the Hepatitis B epidemic model, Results Phys., 26 (2021), 104372. https://doi.org/10.1016/j.rinp.2021.104372 doi: 10.1016/j.rinp.2021.104372
    [11] A. Din, Y. J. Li, Stationary distribution extinction and optimal control for the stochastic Hepatitis B epidemic model with partial immunity, Phys. Scr., 96 (2021), 74005. https://doi.org/10.1088/1402-4896/abfacc doi: 10.1088/1402-4896/abfacc
    [12] B. Wu, J. W. Jia, Asymptotic behavior of a stochastic delayed model for chronic Hepatitis B infection, Complexity, 2020 (2020), 1875475. https://doi.org/10.1155/2020/1875475 doi: 10.1155/2020/1875475
    [13] A. Din, Y. J. Li, A. Yusuf, Delayed Hepatitis B epidemic model with stochastic analysis, Chaos, Solitons Fractals, 146 (2021), 110839. https://doi.org/10.1016/j.chaos.2021.110839 doi: 10.1016/j.chaos.2021.110839
    [14] A. Din, Y. J. Li, Stochastic optimal analysis for the Hepatitis B epidemic model with Markovian switching, Math. Meth. Appl. Sci., 2022 (2022), 1–26. https://doi.org/10.1002/mma.8218 doi: 10.1002/mma.8218
    [15] 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. https://doi.org/10.1137/10081856X doi: 10.1137/10081856X
    [16] I. A. Baba, E. Hincal, Global stability analysis of two-strain epidemic model with bilinear and non-monotone incidence rates, Eur. Phys. J. Plus, 132 (2017), 208. https://doi.org/10.1140/epjp/i2017-11476-x doi: 10.1140/epjp/i2017-11476-x
    [17] J. J. Wang, J. Z. Zhang, Z. Jin, Analysis of an SIR model with bilinear incidence rate, Nonlinear Anal. Real World Appl., 11 (2010), 2390–2402. https://doi.org/10.1016/j.nonrwa.2009.07.012 doi: 10.1016/j.nonrwa.2009.07.012
    [18] T. T. Xue, X. L. Fan, J. Zhu, A class of deterministic and stochastic fractional epidemic models with vaccination, Comput. Math. Methods Med., 2022 (2022), 1–22. https://doi.org/10.1155/2022/1797258 doi: 10.1155/2022/1797258
    [19] Q. Liu, D. Q. Jiang, N. Z. Shi, Threshold behavior in a stochastic SIQR epidemic model with standard incidence and regime switching, Appl. Math. Comput., 316 (2018), 310–325. https://doi.org/10.1016/j.amc.2017.08.042 doi: 10.1016/j.amc.2017.08.042
    [20] T. T. Xue, X. L. Fan, Z. G. Chang, Dynamics of a stochastic SIRS epidemic model with standard incidence and vaccination, Math. Biosci. Eng., 19 (2022), 10618–10636. https://doi.org/10.3934/mbe.2022496 doi: 10.3934/mbe.2022496
    [21] Q. S. Yang, D. Q. Jiang, N. Z. Shi, C. Y. Ji, The ergodicity and extinction of stochastically perturbed SIR and SEIR epidemic models with saturated incidence, J. Math. Anal. Appl., 388 (2012), 248–271. https://doi.org/10.1016/j.jmaa.2011.11.072 doi: 10.1016/j.jmaa.2011.11.072
    [22] Y. N. Zhao, D. Q. Jiang, The threshold of a stochastic SIRS epidemic model with saturated incidence, Appl. Math. Lett., 34 (2014), 90–93. https://doi.org/10.1016/j.aml.2013.11.002 doi: 10.1016/j.aml.2013.11.002
    [23] J. R. Beddington, Mutual interference between parasites or predat ors and its efect on searching efciency, J. Anim. Ecol., 44 (1975), 331–340.
    [24] P. Crowley, E. Martin, Functional responses and interference within and between year classes of a dragonfy population. J. N. Am. Benthol. Soc., 8 (1989), 211–221.
    [25] X. Mao, Stochastic Differential Equations and Applications, Horwood, Chichester, 2007.
    [26] V. N. Afanas'ev, V. B. Kolmanovskii, V. R. Nosov, Mathematical Teory of Control Systems Design, Springer, Dorderecht, The Netherlands, 1996.
    [27] D. Kiouach, Y. Sabbar, Stability and threshold of a stochastic SIRS epidemic model with vertical transmission and transfer from infectious to susceptible individuals, Discrete Dyn. Nat. Soc., 2018 (2018), 7570296. https://doi.org/10.1155/2018/7570296 doi: 10.1155/2018/7570296
    [28] C. Y. Ji, D. Q. Jiang, Threshold behavior of a stochastic SIR model, Appl. Math. Model., 38 (2014), 5067–5079. https://doi.org/10.1016/j.apm.2014.03.037 doi: 10.1016/j.apm.2014.03.037
    [29] G. Zaman, Y. H. Kang, I. H. Jung, Optimal treatment of an SIR epidemic model with time delay, Biosystems, 98 (2009), 43–50. https://doi.org/10.1016/j.biosystems.2009.05.006 doi: 10.1016/j.biosystems.2009.05.006
    [30] M. T. Xia, L. Bottcher, T. Chou, Controlling epidemics through optimal allocation of test kits and vaccine doses across networks, IEEE Trans. Network Sci. Eng., 9 (2022), 1422–1436. https://doi.org/10.1109/TNSE.2022.3144624 doi: 10.1109/TNSE.2022.3144624
    [31] G. Zaman, Y. H. Kang, I. H. Jung, Stability analysis and optimal vaccination of an SIR epidemic model, Biosystems, 93 (2008), 240–249. https://doi.org/10.1016/j.biosystems.2008.05.004 doi: 10.1016/j.biosystems.2008.05.004
    [32] A. V. Kamyad, R. Akbari, A. A. Heydari, A. Heydari, Mathematical modeling of transmission dynamics and optimal control of vaccination and treatment for Hepatitis B virus, Comput. Math. Methods Med., 2014 (2014), 475451. https://doi.org/10.1155/2014/475451 doi: 10.1155/2014/475451
    [33] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelize, E. F. Mishchenko, The Mathematical Theory of Optimal Processes, Wiley, New York, 1962.
  • Reader Comments
  • © 2023 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(1264) PDF downloads(121) Cited by(0)

Article outline

Figures and Tables

Figures(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog