IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119318497.html
   My bibliography  Save this article

Mean-field dispersal induced synchrony and stability in an epidemic model under patchy environment

Author

Listed:
  • Verma, Tina
  • Gupta, Arvind Kumar

Abstract

In our highly connected modern world, the dispersion of population is very important at the time when disease spread to evaluate its nature i.e., whether the disease will die-out or grow. Therefore, in this paper, SIR (Susceptible–Infected–Recovered) model is framed to describe the dispersal of population for different patches (communities) which are interconnected using mean field diffusive coupling. The effect of coupling on the dynamics of the epidemiology classes among different patches has been investigated. The dynamics of the epidemiology classes is explored under the environment of symmetric coupling and asymmetric coupling. It is shown that the synchronization and stability of Disease Free Equilibrium (or Endemic Equilibrium) can be attained simultaneously through the mechanism of suppression of oscillations namely amplitude death (AD) and oscillation death (OD). Irrespective of coupling environment i.e., symmetric and asymmetric, we have observed the tri-stable state and its transition from different states through Hopf bifurcation and transcritical bifurcation. The basic reproduction number is also established for the stability of Disease Free Equilibrium and Endemic Equilibrium. Through bifurcation analysis, it has been found that the disease can become stable or it can die out through dispersal of population among patches.

Suggested Citation

  • Verma, Tina & Gupta, Arvind Kumar, 2020. "Mean-field dispersal induced synchrony and stability in an epidemic model under patchy environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318497
    DOI: 10.1016/j.physa.2019.123300
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119318497
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.123300?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Qun & Jiang, Daqing & Shi, Ningzhong & Hayat, Tasawar & Alsaedi, Ahmed, 2016. "Asymptotic behavior of a stochastic delayed SEIR epidemic model with nonlinear incidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 870-882.
    2. Yakui Xue & Tiantian Li, 2013. "Stability and Hopf Bifurcation for a Delayed SIR Epidemic Model with Logistic Growth," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-11, November.
    3. Sanders, Johnathan & Noble, Benjamin & Van Gorder, Robert A. & Riggs, Cortney, 2012. "Mobility matrix evolution for an SIS epidemic patch model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6256-6267.
    4. Liu, Qun & Jiang, Daqing, 2016. "The threshold of a stochastic delayed SIR epidemic model with vaccination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 140-147.
    5. Liu, Qun & Chen, Qingmei & Jiang, Daqing, 2016. "The threshold of a stochastic delayed SIR epidemic model with temporary immunity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 115-125.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hattaf, Khalid & Mahrouf, Marouane & Adnani, Jihad & Yousfi, Noura, 2018. "Qualitative analysis of a stochastic epidemic model with specific functional response and temporary immunity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 591-600.
    2. Cao, Zhongwei & Shi, Yuee & Wen, Xiangdan & Liu, Liya & Hu, Jingwei, 2020. "Analysis of a hybrid switching SVIR epidemic model with vaccination and Lévy noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Wen, Buyu & Teng, Zhidong & Li, Zhiming, 2018. "The threshold of a periodic stochastic SIVS epidemic model with nonlinear incidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 532-549.
    4. Sun, Shulin & Zhang, Xiaofeng, 2018. "Asymptotic behavior of a stochastic delayed chemostat model with nonmonotone uptake function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 38-56.
    5. Sharma, Natasha & Gupta, Arvind Kumar, 2017. "Impact of time delay on the dynamics of SEIR epidemic model using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 114-125.
    6. Xin, Ming-Zhen & Wang, Bin-Guo, 2020. "Stationary distribution and extinction of a stochastic tuberculosis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    7. Jia, Fangju & Lv, Guangying, 2018. "Dynamic analysis of a stochastic rumor propagation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 613-623.
    8. Nguyen, Dang H. & Nguyen, Nhu N. & Yin, George, 2021. "Stochastic functional Kolmogorov equations, I: Persistence," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 319-364.
    9. Jia, Pingqi & Wang, Chao & Zhang, Gaoyu & Ma, Jianfeng, 2019. "A rumor spreading model based on two propagation channels in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 342-353.
    10. Wanduku, Divine, 2017. "Complete global analysis of a two-scale network SIRS epidemic dynamic model with distributed delay and random perturbations," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 49-76.
    11. Liu, Yue, 2022. "Extinction, persistence and density function analysis of a stochastic two-strain disease model with drug resistance mutation," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    12. Fan, Kuangang & Zhang, Yan & Gao, Shujing & Wei, Xiang, 2017. "A class of stochastic delayed SIR epidemic models with generalized nonlinear incidence rate and temporary immunity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 198-208.
    13. Zhang, Xiao-Bing & Huo, Hai-Feng & Xiang, Hong & Shi, Qihong & Li, Dungang, 2017. "The threshold of a stochastic SIQS epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 362-374.
    14. Chang, Zhengbo & Meng, Xinzhu & Lu, Xiao, 2017. "Analysis of a novel stochastic SIRS epidemic model with two different saturated incidence rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 103-116.
    15. Cai, Yongli & Kang, Yun & Wang, Weiming, 2017. "A stochastic SIRS epidemic model with nonlinear incidence rate," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 221-240.
    16. Berrhazi, Badr-eddine & El Fatini, Mohamed & Laaribi, Aziz, 2018. "A stochastic threshold for an epidemic model with Beddington–DeAngelis incidence, delayed loss of immunity and Lévy noise perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 312-320.
    17. Li, Yan & Ye, Ming & Zhang, Qimin, 2019. "Strong convergence of the partially truncated Euler–Maruyama scheme for a stochastic age-structured SIR epidemic model," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    18. Liu, Qun & Jiang, Daqing & Shi, Ningzhong, 2018. "Threshold behavior in a stochastic SIQR epidemic model with standard incidence and regime switching," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 310-325.
    19. Wei, Fengying & Chen, Lihong, 2020. "Extinction and stationary distribution of an epidemic model with partial vaccination and nonlinear incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    20. Zhou, Baoquan & Han, Bingtao & Jiang, Daqing, 2021. "Ergodic property, extinction and density function of a stochastic SIR epidemic model with nonlinear incidence and general stochastic perturbations," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.