Research article

Finite-time contraction stability of a stochastic reaction-diffusion dengue model with impulse and Markov switching


  • Received: 04 May 2023 Revised: 31 July 2023 Accepted: 08 August 2023 Published: 28 August 2023
  • From the perspective of prevention and treatment of dengue, it is important to minimize the number of infections within a limited time frame. That is, the study of finite time contraction stability (FTCS) of dengue system is a meaningful topic. This article proposes a dengue epidemic model with reaction-diffusion, impulse and Markov switching. By constructing an equivalent system, the well-posedness of the positive solution is proved. The main result is that sufficient conditions to guarantee the finite time contraction stability of the dengue model are acquired based on the average pulse interval method and the bounded pulse interval method. Furthermore, the numerical findings indicate the influences of impulse, control strategies and noise intensity on the FTCS.

    Citation: Wei You, Jie Ren, Qimin Zhang. Finite-time contraction stability of a stochastic reaction-diffusion dengue model with impulse and Markov switching[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16978-17002. doi: 10.3934/mbe.2023757

    Related Papers:

  • From the perspective of prevention and treatment of dengue, it is important to minimize the number of infections within a limited time frame. That is, the study of finite time contraction stability (FTCS) of dengue system is a meaningful topic. This article proposes a dengue epidemic model with reaction-diffusion, impulse and Markov switching. By constructing an equivalent system, the well-posedness of the positive solution is proved. The main result is that sufficient conditions to guarantee the finite time contraction stability of the dengue model are acquired based on the average pulse interval method and the bounded pulse interval method. Furthermore, the numerical findings indicate the influences of impulse, control strategies and noise intensity on the FTCS.



    加载中


    [1] Centers for Diease Control and Prevention, Available from: http://www.cdc.gov/dengue/.
    [2] World Health Organization (WHO), Available from: https://www.who.int/zh/news-room/fact-sheets/detail/dengue-and-severe-dengue.
    [3] Z. Xu, Y. Zhao, A reaction-diffusion model of dengue transmission, Discrete Contin. Dyn. Syst. B, 19 (2014), 2993–3018. https://doi.org/10.3934/dcdsb.2014.19.2993 doi: 10.3934/dcdsb.2014.19.2993
    [4] Y. Li, Y. Wang, L. Liu, Optimal control of dengue vector based on a reaction-diffusion model, Math. Comput. Simul., 203 (2023), 250–270. https://doi.org/10.1016/j.matcom.2022.06.026 doi: 10.1016/j.matcom.2022.06.026
    [5] M. Zhu, Y. Xu, A time-periodic dengue fever model in a heterogeneous environment, Math. Comput. Simul., 155 (2018), 115–129. https://doi.org/10.1016/j.matcom.2017.12.008 doi: 10.1016/j.matcom.2017.12.008
    [6] M. Zhu, Z. Lin, L. Zhang, Spatial-temporal risk index and transmission of a nonlocal dengue model, Nonlinear Anal. Real World Appl., 53 (2020), 103076–103097. https://doi.org/10.1016/j.nonrwa.2019.103076 doi: 10.1016/j.nonrwa.2019.103076
    [7] Y. Pang, S. Wang, S. Liu, Dynamics analysis of stage-structured wild and sterile mosquito interaction impulsive model, J. Biol. Dyn., 16 (2022), 464–479. https://doi.org/10.1080/17513758.2022.2079739 doi: 10.1080/17513758.2022.2079739
    [8] C. X. Yang, L. Nie, The effect of vector control strategy against dengue transmission between mosquitoes and humans, Electron. J. Qual. Theory Differ. Equ., 17 (2017), 1–27. https://doi.org/10.14232/ejqtde.2017.1.17 doi: 10.14232/ejqtde.2017.1.17
    [9] R. Rao, Z. Lin, X. Ai, J. Wu, Synchronization of epidemic systems with Neumann boundary value under delayed impulse, Mathematics, 10 (2022), 2064. https://doi.org/10.3390/math10122064 doi: 10.3390/math10122064
    [10] G. D. Li, Y. Zhang, Y. J. Guan, W. J. Li, Stability analysis of multi-point boundary conditions for fractional differential equation with non-instantaneous integral impulse, Math. Biosci. Eng., 20 (2023), 7020–7041. https://doi.org/10.3934/mbe.2023303 doi: 10.3934/mbe.2023303
    [11] P. Liu, A. Din, Zenab, Impact of information intervention on stochastic dengue epidemic model, Alexandria Eng. J., 60 (2021), 5725–5739. https://doi.org/10.1016/j.aej.2021.03.068 doi: 10.1016/j.aej.2021.03.068
    [12] K. Chang, Q. Zhang, H. Yuan, Stationary distribution and control strategy of a stochastic dengue model with spatial diffusion, J. Appl. Anal. Comput., 12 (2022), 153–178. https://doi.org/10.11948/20210094 doi: 10.11948/20210094
    [13] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, Stationary distribution and extinction of a stochastic dengue epidemic model, J. Franklin Inst., 355 (2018), 8891–8914. https://doi.org/10.1016/j.jfranklin.2018.10.003 doi: 10.1016/j.jfranklin.2018.10.003
    [14] M. Guo, L. Hu, L. F. Nie, Stochastic dynamics of the transmission of Dengue fever virus between mosquitoes and humans, Int. J. Biomath., 14 (2021), 2150062. https://doi.org/10.1142/S1793524521500625 doi: 10.1142/S1793524521500625
    [15] L. Zu, D. Jiang, D. O'Regan, Conditions for persistence and ergodicity of a stochastic Lotka-Volterra predator-prey model with regime switching, Commun. Nonlinear Sci. Numer. Simul., 29 (2015), 1–11. https://doi.org/10.1016/j.cnsns.2015.04.008 doi: 10.1016/j.cnsns.2015.04.008
    [16] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, Stationary distribution of a stochastic within-host dengue infection model with immune response and regime switching, Physica A, 526 (2019), 121057. https://doi.org/10.1016/j.physa.2019.121057 doi: 10.1016/j.physa.2019.121057
    [17] D. Kuang, Q. Yin, J. Li, The threshold of a stochastic SIRS epidemic model with general incidence rate under regime-switching, J. Franklin Inst., 20 (2022), 48. https://doi.org/10.1016/j.jfranklin.2022.04.027 doi: 10.1016/j.jfranklin.2022.04.027
    [18] X. Mu, Q. Zhang, Near-optimal control for a stochastic multi-strain epidemic model with age structure and Markovian switching, Int. J. Control, 95 (2022), 1191–1205. https://doi.org/10.1080/00207179.2020.1843074 doi: 10.1080/00207179.2020.1843074
    [19] A. Gray, D. Greenhalgh, X. Mao, J. Pan, The SIS epidemic model with Markovian switching, J. Math. Anal. Appl., 394 (2012), 496–516. https://doi.org/10.1016/j.jmaa.2012.05.029 doi: 10.1016/j.jmaa.2012.05.029
    [20] S. Kazemi, M. Stommel, L. K. Cheng, W. Xu, Finite-time contraction control of a ring-shaped soft pneumatic actuator mimicking gastric pathologic motility conditions, Soft Rob., 10 (2022). https://doi.org/10.1089/soro.2021.0167 doi: 10.1089/soro.2021.0167
    [21] R. Gan, C. Li, Finite-time stability of nonlinear time-varying systems with saturated impulse inputs, Nonlinear Dyn., 111 (2023), 3497–3507. https://doi.org/10.1007/s11071-022-08024-y doi: 10.1007/s11071-022-08024-y
    [22] K. N. Wu, M. Y. Na, L. Wang, X. Ding, B. Wu, Finite-time stability of impulsive reaction-diffusion systems with and without time delay, Appl. Math. Comput., 363 (2019), 124591. https://doi.org/10.1016/j.amc.2019.124591 doi: 10.1016/j.amc.2019.124591
    [23] Y. Wu, J. Cao, A. Alofi, A. AL-Mazrooei, A. Elaiw, Finite-time boundedness and stabilization of uncertain switched neural networks with time-varying delay, Neural Networks, 69 (2015), 135–143. https://doi.org/10.1016/j.neunet.2015.05.006 doi: 10.1016/j.neunet.2015.05.006
    [24] H. R. Pandey, G. R. Phaijoo, Analysis of dengue infection transmission dynamics in Nepal using fractional order mathematical modeling, Chaos, Solitons Fractals: X, 11 (2023), 100098. https://doi.org/10.1016/j.csfx.2023.100098 doi: 10.1016/j.csfx.2023.100098
    [25] J. Li, H. Wan, M. Sun, Modeling the impact of awareness programs on the transmission dynamics of dengue and optimal control, Int. J. Biomath., 16 (2023), 2250072. https://doi.org/10.1142/S1793524522500723 doi: 10.1142/S1793524522500723
    [26] S. T. Ogunlade, M. T. Meehan, A. I. Adekunle, E. S. McBryde, A systematic review of mathematical models of Dengue transmission and vector control: 2010–2020, Viruses, 15 (2023), 254. https://doi.org/10.3390/v15010254 doi: 10.3390/v15010254
    [27] C. Buhler, V. Winkler, S. Runge-Ranzinger, O. Horstick, Environmental methods for dengue vector control-A systematic review and meta-analysis, PLoS Negl. Trop. Dis., 13 (2019), e0007420. https://doi.org/10.1371/journal.pntd.0007420 doi: 10.1371/journal.pntd.0007420
    [28] T. Pan, D. Jiang, T. Hayat, A. Alsaedi, Extinction and periodic solutions for an impulsive SIR model with incidence rate stochastically perturbed, Physica A, 505 (2018), 385–397. https://doi.org/10.1016/j.physa.2018.03.012 doi: 10.1016/j.physa.2018.03.012
    [29] K. Chang, Q. Zhang, Sufficient and necessary conditions of near-optimal controls for diffusion dengue model with Levy noise, J. Math. Anal. Appl., 514 (2022), 126044. https://doi.org/10.1016/j.jmaa.2022.126044 doi: 10.1016/j.jmaa.2022.126044
    [30] K. Wu, B. S. Chen, Synchronization of partial differential systems via diffusion coupling, IEEE Trans. Circuits Syst. I Regul. Pap., 59 (2012), 2655–2668. https://doi.org/10.1109/tcsi.2012.2190670 doi: 10.1109/tcsi.2012.2190670
    [31] X. Mao, Stochastic functional differential equations with Markovian switching, in Stochastic Differential Equations & Applications, (2011), 147–200. https://doi.org/10.1533/9780857099402.147
    [32] D. D. Bajnov, P. S. Simeonov, Systems with Impulse Effect: Stability, Theory and Applications, E. Horwood, Halsted Press, 1989.
    [33] R. Wu, X. Q. Zhao, A reaction-diffusion model of vector-borne disease with periodic delays, J. Nonlinear Sci., 29 (2019), 29–64. https://doi.org/10.1007/s00332-018-9475-9 doi: 10.1007/s00332-018-9475-9
    [34] H. S. Rodrigues, M. T. T. Monteiro, D. F. M. Torres, Seasonality effects on dengue: basic reproduction number, sensitivity analysis and optimal control, Math. Methods Appl. Sci., 39 (2016), 4671–4679. https://doi.org/10.1002/mma.3319 doi: 10.1002/mma.3319
    [35] W. Hu, Q. Zhu, H. R. Karimi, Some improved Razumikhin stability criteria for impulsive stochastic delay differential systems, IEEE Trans. Autom. Control, 64 (2019), 5207–5213. https://doi.org/10.1109/TAC.2019.2911182 doi: 10.1109/TAC.2019.2911182
    [36] Y. Zhao, L. Wang, Practical exponential stability of impulsive stochastic food chain system with time-varying delays, Mathematics, 11 (2023), 147. https://doi.org/10.3390/math11010147 doi: 10.3390/math11010147
    [37] W. Hu, Q. Zhu, Stability criteria for impulsive stochastic functional differential systems with distributed-delay dependent impulsive effects, IEEE Trans. Syst. Man Cybern. Syst., 51 (2019), 2027–2032. https://doi.org/10.1109/TSMC.2019.2905007 doi: 10.1109/TSMC.2019.2905007
    [38] Y. Tang, L. Zhou, J. Tang, A. Alsaedi, Hybrid impulsive pinning control for mean square Synchronization of uncertain multi-link complex networks with stochastic characteristics and hybrid delays, Mathematics, 11 (2023), 1697. https://doi.org/10.1016/j.physa.2019.121057 doi: 10.1016/j.physa.2019.121057
  • 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(720) PDF downloads(135) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog