Global threshold dynamics in an HIV virus model with nonlinear infection rate and distributed invasion and production delays

  • Received: 01 January 2012 Accepted: 29 June 2018 Published: 01 January 2013
  • MSC : Primary: 92D30; Secondary: 34C23.

  • We consider a mathematical model that describes the interactions ofthe HIV virus, CD4 cells and CTLs within host, which is amodification of some existing models by incorporating (i) twodistributed kernels reflecting the variance of time for virus toinvade into cells and the variance of time for invaded virions toreproduce within cells; (ii) a nonlinear incidence function $f$ forvirus infections, and (iii) a nonlinear removal rate function $h$for infected cells. By constructing Lyapunov functionals and subtleestimates of the derivatives of these Lyapunov functionals, we shownthat the model has the threshold dynamics: if the basicreproduction number (BRN) is less than or equal to one, then theinfection free equilibrium is globally asymptotically stable,meaning that HIV virus will be cleared; whereas if the BRN is largerthan one, then there exist an infected equilibrium which is globallyasymptotically stable, implying that the HIV-1 infection willpersist in the host and the viral concentration will approach apositive constant level. This together with thedependence/independence of the BRN on $f$ and $h$ reveals the effectof the adoption of these nonlinear functions.

    Citation: Zhaohui Yuan, Xingfu Zou. Global threshold dynamics in an HIV virus model with nonlinear infection rate and distributed invasion and production delays[J]. Mathematical Biosciences and Engineering, 2013, 10(2): 483-498. doi: 10.3934/mbe.2013.10.483

    Related Papers:

    [1] A. M. Elaiw, N. H. AlShamrani, A. D. Hobiny . Stability of an adaptive immunity delayed HIV infection model with active and silent cell-to-cell spread. Mathematical Biosciences and Engineering, 2020, 17(6): 6401-6458. doi: 10.3934/mbe.2020337
    [2] A. M. Elaiw, N. H. AlShamrani . Stability of HTLV/HIV dual infection model with mitosis and latency. Mathematical Biosciences and Engineering, 2021, 18(2): 1077-1120. doi: 10.3934/mbe.2021059
    [3] Yu Ji . Global stability of a multiple delayed viral infection model with general incidence rate and an application to HIV infection. Mathematical Biosciences and Engineering, 2015, 12(3): 525-536. doi: 10.3934/mbe.2015.12.525
    [4] Xinran Zhou, Long Zhang, Tao Zheng, Hong-li Li, Zhidong Teng . Global stability for a class of HIV virus-to-cell dynamical model with Beddington-DeAngelis functional response and distributed time delay. Mathematical Biosciences and Engineering, 2020, 17(5): 4527-4543. doi: 10.3934/mbe.2020250
    [5] Ting Guo, Zhipeng Qiu . The effects of CTL immune response on HIV infection model with potent therapy, latently infected cells and cell-to-cell viral transmission. Mathematical Biosciences and Engineering, 2019, 16(6): 6822-6841. doi: 10.3934/mbe.2019341
    [6] Shengqiang Liu, Lin Wang . Global stability of an HIV-1 model with distributed intracellular delays and a combination therapy. Mathematical Biosciences and Engineering, 2010, 7(3): 675-685. doi: 10.3934/mbe.2010.7.675
    [7] Yan Wang, Tingting Zhao, Jun Liu . Viral dynamics of an HIV stochastic model with cell-to-cell infection, CTL immune response and distributed delays. Mathematical Biosciences and Engineering, 2019, 16(6): 7126-7154. doi: 10.3934/mbe.2019358
    [8] A. M. Elaiw, A. S. Shflot, A. D. Hobiny . Stability analysis of general delayed HTLV-I dynamics model with mitosis and CTL immunity. Mathematical Biosciences and Engineering, 2022, 19(12): 12693-12729. doi: 10.3934/mbe.2022593
    [9] Cameron Browne . Immune response in virus model structured by cell infection-age. Mathematical Biosciences and Engineering, 2016, 13(5): 887-909. doi: 10.3934/mbe.2016022
    [10] Jiawei Deng, Ping Jiang, Hongying Shu . Viral infection dynamics with mitosis, intracellular delays and immune response. Mathematical Biosciences and Engineering, 2023, 20(2): 2937-2963. doi: 10.3934/mbe.2023139
  • We consider a mathematical model that describes the interactions ofthe HIV virus, CD4 cells and CTLs within host, which is amodification of some existing models by incorporating (i) twodistributed kernels reflecting the variance of time for virus toinvade into cells and the variance of time for invaded virions toreproduce within cells; (ii) a nonlinear incidence function $f$ forvirus infections, and (iii) a nonlinear removal rate function $h$for infected cells. By constructing Lyapunov functionals and subtleestimates of the derivatives of these Lyapunov functionals, we shownthat the model has the threshold dynamics: if the basicreproduction number (BRN) is less than or equal to one, then theinfection free equilibrium is globally asymptotically stable,meaning that HIV virus will be cleared; whereas if the BRN is largerthan one, then there exist an infected equilibrium which is globallyasymptotically stable, implying that the HIV-1 infection willpersist in the host and the viral concentration will approach apositive constant level. This together with thedependence/independence of the BRN on $f$ and $h$ reveals the effectof the adoption of these nonlinear functions.


    [1] Bioinformatics, 21 (2005), 1668-1677.
    [2] Proc. Roy. Soc. Lond. B, 265 (2000), 1347-1354.
    [3] J. Virol., 71 (1997), 3275-3278.
    [4] Proc. Natl. Acad. Sci. USA, 94 (1997), 6971-6976.
    [5] Chaos, Solitons and Fractals, 12 (2001), 483-489
    [6] in "Mathematics In Science And Engineering" $2^{nd}$ edition, Elsevier, Amsterdam-Boston, 202 (2005).
    [7] Chaos, Solitons and Fractals, 41 (2009), 175-182.
    [8] Bulletin of Mathematical Biology, 64 (2002), 29-64.
    [9] Physica A, 342 (2004), 234-241.
    [10] Math. Biosci., 200 (2006), 1-27.
    [11] J. Math. Biol., 48 (2004), 545-562.
    [12] J. Theoret. Biol., 175 (1995), 567-576.
    [13] J. Theoret. Biol., 190 (1998), 201-214.
    [14] SIAM J. Appl. Math., 67 (2006), 337-353.
    [15] Discrete Continuous Dynam. Systems-B, 4 (2004), 615-622.
    [16] Academic Press, San Diego, 1993.
    [17] Discrete Dynamics in Nature and Society, 2011 (2011), Art. ID 673843, 13 pp.
    [18] Math. Biosci. and Eng., 7 (2010), 675-685.
    [19] Theor. Popul. Biol., 52 (1997), 224-230.
    [20] J. Math. Anal. Appl., 352 (2009), 672-683.
    [21] Math. Biosci., 152 (1998), 143-163.
    [22] J. Math. Anal. Appl., 375 (2011), 14-27.
    [23] Math. Biosci., 163 (2000), 201-215.
    [24] Math. Biosci., 179 (2002), 73-94.
    [25] Science, 272 (1996), 74-79.
    [26] J. Theor. Biol., 184 (1997), 203-217.
    [27] Math. Biosci., 235 (2012), 98-109.
    [28] SIAM Rev., 41 (1999), 3-44.
    [29] Science, 271 (1996), 1582-1586.
    [30] Science, 271 (1996), 497-499.
    [31] Mathematical Medicine and Biology, IMA.
    [32] Comput. Math. Appl., 51 (2006), 1593-1610.
    [33] Physica D, 226 (2007), 197-208.
    [34] Comput. Math. Appl., 61 (2011), 2799-2805.
    [35] J. Math. Anal. Appl., 375 (2011), 75-81.
    [36] Mathematical Medicine and Biology, IMA, 25 (2008), 99-112.
    [37] Discrete Continuous Dynam. Systems-B, 12 (2009), 511-524.
    [38] Comput. Math. Appl., 62 (2011), 3091-3102.
  • This article has been cited by:

    1. David Hiebeler, Moment Equations and Dynamics of a Household SIS Epidemiological Model, 2006, 68, 0092-8240, 1315, 10.1007/s11538-006-9080-1
    2. Kurt Langfeld, Dynamics of epidemic diseases without guaranteed immunity, 2021, 11, 2190-5983, 10.1186/s13362-021-00101-y
    3. Shunjiang Ni, Wenguo Weng, Hui Zhang, Modeling the effects of social impact on epidemic spreading in complex networks, 2011, 390, 03784371, 4528, 10.1016/j.physa.2011.07.042
    4. Constantinos I. Siettos, Lucia Russo, Mathematical modeling of infectious disease dynamics, 2013, 4, 2150-5594, 295, 10.4161/viru.24041
    5. Mike J. Jeger, Marco Pautasso, Ottmar Holdenrieder, Mike W. Shaw, Modelling disease spread and control in networks: implications for plant sciences, 2007, 174, 0028-646X, 279, 10.1111/j.1469-8137.2007.02028.x
    6. Silvio C. Ferreira, Claudio Castellano, Romualdo Pastor-Satorras, Epidemic thresholds of the susceptible-infected-susceptible model on networks: A comparison of numerical and theoretical results, 2012, 86, 1539-3755, 10.1103/PhysRevE.86.041125
    7. Shunjiang Ni, Wenguo Weng, Shifei Shen, Weicheng Fan, Epidemic outbreaks in growing scale-free networks with local structure, 2008, 387, 03784371, 5295, 10.1016/j.physa.2008.05.051
    8. Carlo Piccardi, Renato Casagrandi, 2009, Chapter 5, 978-3-642-03198-4, 77, 10.1007/978-3-642-03199-1_5
    9. Shaofen Zou, Jianhong Wu, Yuming Chen, Multiple epidemic waves in delayed susceptible-infected-recovered models on complex networks, 2011, 83, 1539-3755, 10.1103/PhysRevE.83.056121
    10. David E. Hiebeler, Andrew Audibert, Emma Strubell, Isaac J. Michaud, An epidemiological model of internet worms with hierarchical dispersal and spatial clustering of hosts, 2017, 418, 00225193, 8, 10.1016/j.jtbi.2017.01.035
    11. Wei-Ping Guo, Xiang Li, Xiao-Fan Wang, Epidemics and immunization on Euclidean distance preferred small-world networks, 2007, 380, 03784371, 684, 10.1016/j.physa.2007.03.007
    12. Tad Dallas, Stephanie Foré, Chemical attraction of Dermacentor variabilis ticks parasitic to Peromyscus leucopus based on host body mass and sex, 2013, 61, 0168-8162, 243, 10.1007/s10493-013-9690-x
    13. David E. Hiebeler, Amanda Keck Criner, Partially mixed household epidemiological model with clustered resistant individuals, 2007, 75, 1539-3755, 10.1103/PhysRevE.75.022901
    14. Andreas I. Reppas, Konstantinos Spiliotis, Constantinos I. Siettos, On the effect of the path length of small-world networks on epidemic dynamics, 2012, 3, 2150-5594, 146, 10.4161/viru.19131
    15. Carlo Piccardi, Renato Casagrandi, Inefficient epidemic spreading in scale-free networks, 2008, 77, 1539-3755, 10.1103/PhysRevE.77.026113
    16. Ramakant Prasad, Surendra Kumar Sagar, Shama Parveen, Ravins Dohare, Mathematical modeling in perspective of vector-borne viral infections: a review, 2022, 11, 2314-8543, 10.1186/s43088-022-00282-4
    17. Wenbin Gu, Wenjie Li, Feng Gao, Sheng Su, Baolin Sun, Wei Wang, Influence of human motion patterns on epidemic spreading dynamics, 2024, 34, 1054-1500, 10.1063/5.0158243
    18. Wu Wang, Cong Li, Bo Qu, Xiang Li, Predicting epidemic threshold in complex networks by graph neural network, 2024, 34, 1054-1500, 10.1063/5.0209912
  • Reader Comments
  • © 2013 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(3561) PDF downloads(668) Cited by(32)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog