Research article

Threshold dynamics and optimal control on an age-structured SIRS epidemic model with vaccination

  • Received: 24 September 2021 Accepted: 25 October 2021 Published: 29 October 2021
  • We consider a vaccination control into a age-structured susceptible-infective-recovered-susceptible (SIRS) model and study the global stability of the endemic equilibrium by the iterative method. The basic reproduction number $ R_0 $ is obtained. It is shown that if $ R_0 < 1 $, then the disease-free equilibrium is globally asymptotically stable, if $ R_0 > 1 $, then the disease-free and endemic equilibrium coexist simultaneously, and the global asymptotic stability of endemic equilibrium is also shown. Additionally, the Hamilton-Jacobi-Bellman (HJB) equation is given by employing the Bellman's principle of optimality. Through proving the existence of viscosity solution for HJB equation, we obtain the optimal vaccination control strategy. Finally, numerical simulations are performed to illustrate the corresponding analytical results.

    Citation: Han Ma, Qimin Zhang. Threshold dynamics and optimal control on an age-structured SIRS epidemic model with vaccination[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9474-9495. doi: 10.3934/mbe.2021465

    Related Papers:

  • We consider a vaccination control into a age-structured susceptible-infective-recovered-susceptible (SIRS) model and study the global stability of the endemic equilibrium by the iterative method. The basic reproduction number $ R_0 $ is obtained. It is shown that if $ R_0 < 1 $, then the disease-free equilibrium is globally asymptotically stable, if $ R_0 > 1 $, then the disease-free and endemic equilibrium coexist simultaneously, and the global asymptotic stability of endemic equilibrium is also shown. Additionally, the Hamilton-Jacobi-Bellman (HJB) equation is given by employing the Bellman's principle of optimality. Through proving the existence of viscosity solution for HJB equation, we obtain the optimal vaccination control strategy. Finally, numerical simulations are performed to illustrate the corresponding analytical results.



    加载中


    [1] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. London, Ser. A, 115 (1927), 700–721. doi: 10.1098/rspa.1927.0118
    [2] N. P. Ahmad, Global dynamics of a fractional-order SIR epidemic model with memory, Int. J. Biomath., 13 (2020), 500710.
    [3] P. A. Naik, J. Zu, K. M. Owolabi, Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control, Chaos Solitons Fractals, 138 (2020), 109826. doi: 10.1016/j.chaos.2020.109826
    [4] P. A. Naik, J. Zu, K. M. Owolabi, Modeling the mechanics of viral kinetics under immune control during primary infection of HIV-1 with treatment in fractional order, Physica A, 545 (2020), 123816. doi: 10.1016/j.physa.2019.123816
    [5] J. Zu, M. Li, Y. Gu, S. Fu, Modelling the evolutionary dynamics of host resistance-related traits in a susceptible-infected community with density-dependent mortality, Discrete. Contin. Dyn. Syst. Ser. B, 25 (2020), 3049–3086.
    [6] Y. Enatsu, Y. Nakata, Y. Muroya, Global stability of SIRS epidemic models with a class of nonlinear incidence rates and distributed delays, Acta. Math. Sci., 32 (2012), 851–865. doi: 10.1016/S0252-9602(12)60066-6
    [7] A. G. M'Kendrick, Applications of mathematics to medical problems, Proc. Edinburgh. Math. Soc., 44 (1925), 98–130.
    [8] K. Cooke, S. Busenberg, Vertically transmitted disease, in Nonlinear Phenomena in Mathematical Sciences (Edited by V. Lakshmikantham), Academic Press, New York, 1982.
    [9] J. Zu, M. Li, G. Zhuang, P. Liang, F. Cui, F. Wang, et al., Estimating the impact of test-and-treat strategies on hepatitis B virus infection in China by using an age-structured mathematical model, Medicine, 97 (2018), e0484. doi: 10.1097/MD.0000000000010484
    [10] J. Yang, Z. Jin, L. Wang, F. Xu, A note on an age-of-infection SVIR model with nonlinear incidence, Int. J. Biomath., 10 (2017), 500644.
    [11] T. Kuniya, Global stability analysis with a discretization approach for an age-structured multi-group SIR epidemic model, Nonlinear Anal. Real World Appl., 12 (2011), 2640–2655. doi: 10.1016/j.nonrwa.2011.03.011
    [12] Z. He, J. Cheng, C. Zhang, Optimal birth control of age-dependent competitive species, J. Math. Anal. Appl., 296 (2008), 286–301.
    [13] J. Yang, Z. Jin, F. Xu, Threshold dynamics of an age-space structured SIR model on heterogeneous environment, Appl. Math. Lett., 96 (2019), 69–74. doi: 10.1016/j.aml.2019.03.009
    [14] F. Yang, L. Yuan, X. Tan, C. Huang, J. Feng, Bayesian estimation of the effective reproduction number for pandemic influenza a H1N1 in Guangdong province, Ann. Epidemiol., 23 (2013), 301–306. doi: 10.1016/j.annepidem.2013.04.005
    [15] P. A. Naik, J. Zu, M. Ghoreishi, Stability analysis and approximate solution of SIR epidemic model with Crowley-Martin type functional response and holling type-Ⅱ treatment rate by using homotopy analysis method, J. Appl. Anal. Comput., 10 (2020), 1482–1515.
    [16] G. Lan, S. Yuan, B. Song, The impact of hospital resources and environmental perturbations to the dynamics of SIRS model, J. Franklin Ins., 358 (2021), 2405–2433. doi: 10.1016/j.jfranklin.2021.01.015
    [17] C. Abdennasser, F. M. Nor, K. Toshikazu, T. T. Mohammed, Global stability of an age-structured epidemic model with general Lyapunov functional, Math. Biosci. Eng., 16 (2019), 1525–1553. doi: 10.3934/mbe.2019073
    [18] X. Mu, Q. Zhang, Near-optimal control for a stochastic multi-strain epidemic model with age structure and Markovian switching, Int. J. Control, 2020.
    [19] L. Bolzoni, E. Bonacini, R. D. Marca, M. Groppi, Optimal control of epidemic size and duration with limited resources, Math. Biosci., 315 (2019), 108232. doi: 10.1016/j.mbs.2019.108232
    [20] Y. Zhou, J. Wu, M. Wu, Optimal isolation strategies of emerging infectious diseases with limited resources, Math. Biosci. Eng., 10 (2013), 1691–1701. doi: 10.3934/mbe.2013.10.1691
    [21] H. Behncke, Optimal control of deterministic epidemics, Optim. Control Appl. Methods, 21 (2000), 269–285. doi: 10.1002/oca.678
    [22] B. Luca, B. Elena, S. Cinzia, G. Maria, Time-optimal control strategies in SIR epidemic models, Math. Biosci., 292 (2017), 86–96. doi: 10.1016/j.mbs.2017.07.011
    [23] E. Hansen, T. Day, Optimal control of epidemics with limited resources, J. Math. Biol., 62 (2011), 423–451. doi: 10.1007/s00285-010-0341-0
    [24] M. T. Meehan, D. G. Cocks, J. M. Trauer, E. S. McBryde, Coupled, multi-strain epidemic models of mutating pathogens, Math. Biol., 296 (2018), 82–92.
    [25] S. Lenhart, J. T. Workman, Optimal control applied to biological models, Crc. Press, 2007.
    [26] S. N. Busenberg, M. Iannelli, H. R. Thieme, Global behavior of an age-structured epidemic model, SIAM J. Math. Anal., 22 (2006), 522069.
    [27] T. Cheng, F. L. Lewis, M. Abu-Khalaf, A neural network solution for fixed-final time optimal control of nonlinear systems, Automatica, 43 (2006), 482–490.
    [28] S. M. Mirhosseini-Alizamini, S. Effati, A. Heydari, An iterative method for suboptimal control of linear time-delayed system, Syst. Control Lett., 82 (2015), 40–50. doi: 10.1016/j.sysconle.2015.04.013
    [29] Z. Zhao, Y. Yang, H. Li, D. Liu, Approximate finite-horizon optimal control with policy iteration, IEEE Proc. 33rd Chinese Control Conference (CCC), 2014, 8889–8894.
    [30] Q. Zhao, H. Xu, S. Jagannathan, Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems, IEEE Trans. Neural Netw. Learn. Syst., 26 (2015), 486–499. doi: 10.1109/TNNLS.2014.2315646
    [31] H. Xu, Q. Zhao, J. Sarangapani, Neural network-based finite-horizon approximately optimal control of uncertain affine nonlinear continuous-time systems, IEEE 2014 American Control Conference (ACC), 2014, 1243–1248.
    [32] H. Xu, S. Jagannathan, Neural network-based finite horizon stochastic optimal control design for nonlinear network control systems, IEEE Trans. Neural Netw. Learn. Syst., 26 (2015), 472–485. doi: 10.1109/TNNLS.2014.2315622
    [33] Q. Zhao, H. Xu, T. Dierks, S. Jagannathan, Finite-horizon network-based optimal control design for affine nonlinear continuous-time systems, IEEE International Joint Conference on Neural Networks (IJCNN), 2013, 4–9.
    [34] J. Yong, X. Y. Zhou, Stochastic controls: Hamiltonian systems and HJB equations, Springer Science Business Media, 1999.
    [35] E. A. Murray, M. M. Seyed, R. Gergely, J. Wu, A delay differential model for pandemic influenza with antiviral treatment, Bull. Math. Biol., 70 (2007), 382–397.
    [36] D. Lukes, Differential Equations: Classical to controlled, In Math. Sci. Eng., 1982.
    [37] S. Wu, L. Chen, C. Hsu, Traveling wave solutions for a diffusive age-structured SIR epidemic model, Commun. Nonlinear Sci. Numer. Simul., 98 (2021), 105769. doi: 10.1016/j.cnsns.2021.105769
  • Reader Comments
  • © 2021 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(2677) PDF downloads(163) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog