Research article Special Issues

Analysis of a two-patch SIS model with saturating contact rate and one- directing population dispersal


  • Received: 15 June 2022 Revised: 15 July 2022 Accepted: 20 July 2022 Published: 05 August 2022
  • In this paper, a two-patch SIS model with saturating contact rate and one-directing population dispersal is proposed. In the model, individuals can only migrate from patch 1 to patch 2. The basic reproduction number $ R_0^1 $ of patch 1 and the basic reproduction number $ R_0^2 $ of patch 2 is identified. The global dynamics are completely determined by the two reproduction numbers. It is shown that if $ R_0^1 < 1 $ and $ R_0^2 < 1 $, the disease-free equilibrium is globally asymptotically stable; if $ R_0^1 < 1 $ and $ R_0^2 > 1 $, there is a boundary equilibrium which is globally asymptotically stable; if $ R_0^1 > 1 $, there is a unique endemic equilibrium which is globally asymptotically stable. Finally, numerical simulations are performed to validate the theoretical results and reveal the influence of saturating contact rate and migration rate on basic reproduction number and the transmission scale.

    Citation: Ruixia Zhang, Shuping Li. Analysis of a two-patch SIS model with saturating contact rate and one- directing population dispersal[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11217-11231. doi: 10.3934/mbe.2022523

    Related Papers:

  • In this paper, a two-patch SIS model with saturating contact rate and one-directing population dispersal is proposed. In the model, individuals can only migrate from patch 1 to patch 2. The basic reproduction number $ R_0^1 $ of patch 1 and the basic reproduction number $ R_0^2 $ of patch 2 is identified. The global dynamics are completely determined by the two reproduction numbers. It is shown that if $ R_0^1 < 1 $ and $ R_0^2 < 1 $, the disease-free equilibrium is globally asymptotically stable; if $ R_0^1 < 1 $ and $ R_0^2 > 1 $, there is a boundary equilibrium which is globally asymptotically stable; if $ R_0^1 > 1 $, there is a unique endemic equilibrium which is globally asymptotically stable. Finally, numerical simulations are performed to validate the theoretical results and reveal the influence of saturating contact rate and migration rate on basic reproduction number and the transmission scale.



    加载中


    [1] F. Brauer, P. van den Driessche, Models for transmission of disease with immigration of infectives, Math. Biosci., 171 (2001), 143–154. https://doi.org/10.1016/S0025-5564(01)00057-8 doi: 10.1016/S0025-5564(01)00057-8
    [2] W. Wang, G. Mulone, Threshold of disease transmission in a patch environment, J. Math. Anal. Appl., 285 (2003), 321–335. https://doi.org/10.1016/S0022-247X(03)00428-1 doi: 10.1016/S0022-247X(03)00428-1
    [3] C. Sun, W. Yang, J. Arino, K. Khan, Effect of media-induced social distancing on disease transmission in a two patchsetting, Math. Biosci., 230 (2011), 87–95. https://doi.org/10.1016/j.mbs.2011.01.005 doi: 10.1016/j.mbs.2011.01.005
    [4] X. Feng, L. Liu, S. Tang, X. Huo, Stability and bifurcation analysis of a two-patch SIS model on nosocomial infections, Appl. Math. Lett., 102 (2020), 106097. https://doi.org/10.1016/j.aml.2019.106097 doi: 10.1016/j.aml.2019.106097
    [5] D. Gao, S. Ruan, An SIS patch model with variable transmission coefficients, Math. Biosci., 232 (2011), 110–115. https://doi.org/10.1016/j.mbs.2011.05.001 doi: 10.1016/j.mbs.2011.05.001
    [6] D. Gao, C. Cosner, R. S. Cantrell, J. C. Beier, S. Ruan, Modeling the spatial spread of rift valley fever in egypt, Bull. Math. Biol., 75 (2013), 523–542. https://doi.org/10.1007/s11538-013-9818-5 doi: 10.1007/s11538-013-9818-5
    [7] V. Capasso, G. Serio, A generalization of the Kermack-Mckendrick deterministic epidemic model, Math. Biosci., 42 (1978), 41–61. https://doi.org/10.1016/0025-5564(78)90006-8 doi: 10.1016/0025-5564(78)90006-8
    [8] M. P. Coffee, G. P. Garnett, M. Mlilo, H. A. C. M. Voeten, S. Chandiwana, S. Gregson, Patterns of movement and risk of HIV infection in rural Zimbabwe, J. Infect. Dis., 191 (2005), 159–167. https://doi.org/10.1086/425270 doi: 10.1086/425270
    [9] 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
    [10] Z. Jiang, J. Wei, Stability and bifurcation analysis in a delayed SIR model, Chaos, Solitons Fractals, 25 (2008), 609–619. https://doi.org/10.1016/j.chaos.2006.05.045 doi: 10.1016/j.chaos.2006.05.045
    [11] R. Xu, Z. Ma, Stability of a delayed SIRS epidemic model with a nonlinear incidence rate, Chaos, Solitons Fractals, 41 (2009), 2319–2325. https://doi.org/10.1016/j.chaos.2008.09.007 doi: 10.1016/j.chaos.2008.09.007
    [12] Z. Zhang, Y. Suo, Qualitative analysis of a SIR epidemic model with saturated treatment rate, J. Appl. Math. Comput., 34 (2010), 177–194. https://doi.org/10.1007/s12190-009-0315-9 doi: 10.1007/s12190-009-0315-9
    [13] S. Liu, Y. Pei, C. Li, L. Chen, Three kinds of TVS in a SIR epidemic model with saturated, infectious force and vertical transmission, Appl. Math. Model., 33 (2009), 1923–1932. https://doi.org/10.1016/j.apm.2008.05.001 doi: 10.1016/j.apm.2008.05.001
    [14] A. K. Nilam, Mathematical analysis of a delayed epidemic model with nonlinear incidence and treatment rates, J. Eng. Math., 115 (2019), 1–20. https://doi.org/10.1007/s10665-019-09989-3 doi: 10.1007/s10665-019-09989-3
    [15] K. G. Nilam, Stability behavior of a nonlinear mathematical epidemic transmission model with time delay, Nonlinear Dyn., 98 (2019), 1501–1518. https://doi.org/10.1007/s11071-019-05276-z doi: 10.1007/s11071-019-05276-z
    [16] K. G. Nilam, A mathematical and numerical study of a SIR epidemic model with time delay, nonlinear incidence and treatment rates, Theory Biosci., 138 (2019), 203–213. https://doi.org/10.1007/s12064-019-00275-5 doi: 10.1007/s12064-019-00275-5
    [17] Z. Liu, Dynamics of positive solutions to SIR and SEIR epidemic models with saturated incidence rates, Nonlinear Anal.: Real World Appl., 14 (2013), 1286–1289. https://doi.org/10.1016/j.nonrwa.2012.09.016 doi: 10.1016/j.nonrwa.2012.09.016
    [18] M. E. Fatini, I. Sekkak, A. Laaribi, A threshold of a delayed stochastic epidemic model with Crowly-Martin functional response and vaccination, Phys. A, 520 (2019), 151–160. https://doi.org/10.1016/j.physa.2019.01.014 doi: 10.1016/j.physa.2019.01.014
    [19] R. K. Upadhyay, A. K. Pal, S. Kumari, P. Roy, Dynamics of an SEIR epidemic model with nonlinear incidence and treatment rates, Nonlinear Dyn., 96 (2019), 2351–2368. https://doi.org/10.1007/s11071-019-04926-6 doi: 10.1007/s11071-019-04926-6
    [20] P. van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
    [21] H. L. Smith, P. Waltman, The Theory of the Chemostat, Cambridge University Press, 1995. https: //doi.org/10.1017/CBO9780511530043
    [22] Y. Wang, Z. Wei, J. Cao, Epidemic dynamics of influenza-like diseases spreading in complex networks, Nonlinear Dyn., 101 (2020), 1801–1820. https://doi.org/10.1007/s11071-020-05867-1 doi: 10.1007/s11071-020-05867-1
    [23] R. Pastor-Satorras, C. Castellano, P. Van Mieghem, A. Vespignani, Epidemic processes in complex networks, Rev. Mod. Phys., 87 (2015), 925–979. https://doi.org/10.1103/RevModPhys.87.925 doi: 10.1103/RevModPhys.87.925
  • Reader Comments
  • © 2022 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(1176) PDF downloads(79) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog