Research article

Dynamics of a stochastic epidemic model with quarantine and non-monotone incidence

  • Received: 09 December 2022 Revised: 03 March 2023 Accepted: 08 March 2023 Published: 03 April 2023
  • MSC : 34D30, 60H10, 92D25

  • In this paper, a stochastic SIQR epidemic model with non-monotone incidence is investigated. First of all, we consider the disease-free equilibrium of the deterministic model is globally asymptotically stable by using the Lyapunov method. Secondly, the existence and uniqueness of positive solution to the stochastic model is obtained. Then, the sufficient condition for extinction of the stochastic model is established. Furthermore, a unique stationary distribution to stochastic model will exist by constructing proper Lyapunov function. Finally, numerical examples are carried out to illustrate the theoretical results, with the help of numerical simulations, we can see that the higher intensities of the white noise or the bigger of the quarantine rate can accelerate the extinction of the disease. This theoretically explains the significance of quarantine strength (or isolation measures) when an epidemic erupts.

    Citation: Tingting Wang, Shulin Sun. Dynamics of a stochastic epidemic model with quarantine and non-monotone incidence[J]. AIMS Mathematics, 2023, 8(6): 13241-13256. doi: 10.3934/math.2023669

    Related Papers:

  • In this paper, a stochastic SIQR epidemic model with non-monotone incidence is investigated. First of all, we consider the disease-free equilibrium of the deterministic model is globally asymptotically stable by using the Lyapunov method. Secondly, the existence and uniqueness of positive solution to the stochastic model is obtained. Then, the sufficient condition for extinction of the stochastic model is established. Furthermore, a unique stationary distribution to stochastic model will exist by constructing proper Lyapunov function. Finally, numerical examples are carried out to illustrate the theoretical results, with the help of numerical simulations, we can see that the higher intensities of the white noise or the bigger of the quarantine rate can accelerate the extinction of the disease. This theoretically explains the significance of quarantine strength (or isolation measures) when an epidemic erupts.



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    [1] J. Chan, S. Yuan, K. H. Kok, K. K. Wang, H. Chu, J. Yang, et al., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster, The Lancet, 395 (2020), 514–523. https://doi.org/10.1016/S0140-6736(20)30154-9 doi: 10.1016/S0140-6736(20)30154-9
    [2] W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics-Ⅰ, Bull. Math. Biol., 53 (1991), 33–55. https://doi.org/10.1007/BF02464423 doi: 10.1007/BF02464423
    [3] Q. 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 (2017), 248–271. https://doi.org/10.1016/j.jmaa.2011.11.072 doi: 10.1016/j.jmaa.2011.11.072
    [4] H. Huo, P. Yang, H. Xiang, Stability and bifurcation for an SEIS epidemic model with the impact of media, Physica A Stat. Mech. Appl., 490 (2018), 702–720. https://doi.org/10.1016/j.physa.2017.08.139 doi: 10.1016/j.physa.2017.08.139
    [5] Y. Zhao, D. Jiang, The threshold of a stochastic SIS epidemic model with vaccination, Appl. Math. Comput., 243 (2014), 718–727. https://doi.org/10.1016/j.amc.2014.05.124 doi: 10.1016/j.amc.2014.05.124
    [6] T. Odagaki, Exact properties of SIQR model for COVID-19, Physica A Stat. Mech. Appl., 564 (2021), 125564. https://doi.org/10.1016/j.physa.2020.125564 doi: 10.1016/j.physa.2020.125564
    [7] S. Jain, S. Kumar, Dynamic analysis of the role of innate immunity in SEIS epidemic model, Eur. Phys. J. Plus, 136 (2021), 439. https://doi.org/10.1140/epjp/s13360-021-01390-3 doi: 10.1140/epjp/s13360-021-01390-3
    [8] A. Omar, Y. Alnafisah, R. A. Elbarkouky, H. M. Ahmed, COVID-19 deterministic and stochastic modeling with optimized daily vaccinations in Saudi Arabia, Results Phys., 28 (2021), 104629. https://doi.org/10.1016/j.rinp.2021.104629 doi: 10.1016/j.rinp.2021.104629
    [9] A. omar, R. A. Elbarkouky, H. M. Ahmed, Fractional stochastic modelling of COVID-19 under wide spread of vaccinations: Egyptian case study, Alexandrian Eng. J., 61 (2022), 8595–8609. https://doi.org/10.1016/j.aej.2022.02.002 doi: 10.1016/j.aej.2022.02.002
    [10] R. Din, E. A. Algehyne, Mathematical analysis of COVID-19 by using SIR model with convex incidence rate, Results Phys., 23 (2021), 103970. https://doi.org/10.1016/j.rinp.2021.103970 doi: 10.1016/j.rinp.2021.103970
    [11] O. Nave, U. Shemesh, I. HarTuv, Applizing Laplace Adomain decomposition method (LADM) for solving a model of COVID-19, Comput. Method. Biomec. Biomed. Eng., 24 (2021), 1618–1628. https://doi.org/10.1080/10255842.2021.1904399 doi: 10.1080/10255842.2021.1904399
    [12] World Health Organization, World health organization, contact tracing in the context of COVID-19, 2021. Available from: https://www.who.int/fr/publications-detail/contact tracing in the context of covid-19.
    [13] G. Zhang, Z. Li, A. Din, A stochastic SIQR epidemic model with L$\acute{e}$vy jumps and three-time delays, Appl. Math. Comput., 431 (2022), 127329. https://doi.org/10.1016/j.amc.2022.127329 doi: 10.1016/j.amc.2022.127329
    [14] Y. Ma, J. Liu, H. Li, Global dynamics of an SIQR model with vaccination and elimination hybrid strategies, Mathematics, 6 (2018), 328. https://doi.org/10.3390/math6120328 doi: 10.3390/math6120328
    [15] X. Zhang, R. Liu, The stationary distribution of a stochastic SIQS epidemic model with varying total population size, Appl. Math. Lett., 116 (2021), 106974. https://doi.org/10.1016/j.aml.2020.106974 doi: 10.1016/j.aml.2020.106974
    [16] X. Zhang, H. Huo, H. Xiang, X. Meng, Dynamics of the deterministic and stochastic SIQS epidemic model with non-linear incidence, Appl. Math. Comput., 243 (2014), 546–558. https://doi.org/10.1016/j.amc.2014.05.136 doi: 10.1016/j.amc.2014.05.136
    [17] Q. Liu, D. Jiang, N. Shi, Threshold behavior in a stochastic SIQR epidemic model with stanadard 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
    [18] S. Ruschel, T. Pereira, S. Yanchuk, L. Young, An SIQ delay differential equations model for disease control via isolation, J. Math. Biol., 79 (2019), 249–279. https://doi.org/10.1007/s00285-019-01356-1 doi: 10.1007/s00285-019-01356-1
    [19] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, Dynamics of a stochastic multigroup SIQR epidemic model with standard incidence rates, J. Franklin Inst., 356 (2019), 2960–2993. https://doi.org/10.1016/j.jfranklin.2019.01.038 doi: 10.1016/j.jfranklin.2019.01.038
    [20] V. Capasso, G. Serio, A generalization of the Kermack-Mckendrick deterministic epidemic model, Math. Biosci., 42 (1978), 43–61. https://doi.org/10.1016/0025-5564(78)90006-8 doi: 10.1016/0025-5564(78)90006-8
    [21] S. Ruan, W. Wang, Dynamical behavior of an epidemic model with a nonlinear incidence rate, J. Differ. Equ., 188 (2003), 135–163. https://doi.org/10.1016/S0022-0396(02)00089-X doi: 10.1016/S0022-0396(02)00089-X
    [22] D. Xiao, S. Ruan, Global analysis of an epidemic model with nonmonotone incidence rate, Math. Biosci., 208 (2007), 419–429. https://doi.org/10.1016/j.mbs.2006.09.025 doi: 10.1016/j.mbs.2006.09.025
    [23] A. B. Gumel, S. Ruan, T. Day, J. Watmough, F. Brauer, P. van den Driessche, et al., Modelling strategies for controlling SARS outbreaks, Proc. R. Soc. Lond. B., 271 (2004), 2223–2232. https://doi.org/10.1098/rspb.2004.2800 doi: 10.1098/rspb.2004.2800
    [24] D. Li, J. Cui, M. Liu, S. Liu, The evolutionary dynamics of stochastic epidemic model with nonlinear incidence rate, Bull. Math. Biol., 77 (2015), 1705–1743. https://doi.org/10.1007/s11538-015-0101-9 doi: 10.1007/s11538-015-0101-9
    [25] G. Lan, S. Yuan, B. Song, The impact of hospital resources and environmental perturbations to the dynamics of SIRS model, J. Franklin Inst., 358 (2021), 2405–2433. https://doi.org/10.1016/j.jfranklin.2021.01.015 doi: 10.1016/j.jfranklin.2021.01.015
    [26] P. van den Driessche, Reproduction numbers of infectious disease models, Infect. Dis. Model., 2 (2017), 288–303. https://doi.org/10.1016/j.idm.2017.06.002 doi: 10.1016/j.idm.2017.06.002
    [27] X. R. Mao, Stochastic differential equations and applications, Cambridge: Woodhead Publishing, 2011.
    [28] Y. Cai, Y. Kang, W. Wang, A stochastic SIRS epidemic model with nonlinear incidence rate, Appl. Math. Comput., 305 (2017), 221–240. https://doi.org/10.1016/j.amc.2017.02.003 doi: 10.1016/j.amc.2017.02.003
    [29] R. Khasminskii, Stochastic stability of differential equations, Berlin: Springer, 2012. https://doi.org/10.1007/978-3-642-23280-0
    [30] D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, SIAM Rev., 43 (2001), 525–546. https://doi.org/10.1137/S0036144500378302 doi: 10.1137/S0036144500378302
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