Research article

Analysis of an HTLV/HIV dual infection model with diffusion

  • Received: 10 August 2021 Accepted: 12 October 2021 Published: 28 October 2021
  • In the literature, several HTLV-I and HIV single infections models with spatial dependence have been developed and analyzed. However, modeling HTLV/HIV dual infection with diffusion has not been studied. In this work we derive and investigate a PDE model that describes the dynamics of HTLV/HIV dual infection taking into account the mobility of viruses and cells. The model includes the effect of Cytotoxic T lymphocytes (CTLs) immunity. Although HTLV-I and HIV primarily target the same host, CD$ 4^{+} $T cells, via infected-to-cell (ITC) contact, however the HIV can also be transmitted through free-to-cell (FTC) contact. Moreover, HTLV-I has a vertical transmission through mitosis of active HTLV-infected cells. The well-posedness of solutions, including the existence of global solutions and the boundedness, is justified. We derive eight threshold parameters which govern the existence and stability of the eight steady states of the model. We study the global stability of all steady states based on the construction of suitable Lyapunov functions and usage of Lyapunov-LaSalle asymptotic stability theorem. Lastly, numerical simulations are carried out in order to verify the validity of our theoretical results.

    Citation: A. M. Elaiw, N. H. AlShamrani. Analysis of an HTLV/HIV dual infection model with diffusion[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9430-9473. doi: 10.3934/mbe.2021464

    Related Papers:

  • In the literature, several HTLV-I and HIV single infections models with spatial dependence have been developed and analyzed. However, modeling HTLV/HIV dual infection with diffusion has not been studied. In this work we derive and investigate a PDE model that describes the dynamics of HTLV/HIV dual infection taking into account the mobility of viruses and cells. The model includes the effect of Cytotoxic T lymphocytes (CTLs) immunity. Although HTLV-I and HIV primarily target the same host, CD$ 4^{+} $T cells, via infected-to-cell (ITC) contact, however the HIV can also be transmitted through free-to-cell (FTC) contact. Moreover, HTLV-I has a vertical transmission through mitosis of active HTLV-infected cells. The well-posedness of solutions, including the existence of global solutions and the boundedness, is justified. We derive eight threshold parameters which govern the existence and stability of the eight steady states of the model. We study the global stability of all steady states based on the construction of suitable Lyapunov functions and usage of Lyapunov-LaSalle asymptotic stability theorem. Lastly, numerical simulations are carried out in order to verify the validity of our theoretical results.



    加载中


    [1] M. A. Nowak, R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, Oxford, 2000.
    [2] M. A. Nowak, C. R. M. Bangham, Population dynamics of immune responses to persistent viruses, Science, 272 (1996), 74–79. doi: 10.1126/science.272.5258.74
    [3] A. S. Perelson, P. Essunger, Y. Cao, M. Vesanen, A. Hurley, K. Saksela, et al., Decay characteristics of HIV-1-infected compartments during combination therapy, Nature, 387 (1997), 188–191. doi: 10.1038/387188a0
    [4] A. S. Perelson, P. W. Nelson, Mathematical analysis of HIV-1 dynamics in vivo, SIAM Rev., 41 (1999), 3–44. doi: 10.1137/S0036144598335107
    [5] R. V. Culshaw, S. Ruan, A delay-differential equation model of HIV infection of CD$4^{+}$ T-cells, Math. Biosci., 165 (2000), 27–39. doi: 10.1016/S0025-5564(00)00006-7
    [6] D. S. Callaway, A. S. Perelson, HIV-1 infection and low steady state viral loads, Bull. Math. Biol., 64 (2002), 29–64. doi: 10.1006/bulm.2001.0266
    [7] A. M. Elaiw, Global properties of a class of HIV models, Nonlinear Anal. Real World Appl., 11 (2010), 2253–2263. doi: 10.1016/j.nonrwa.2009.07.001
    [8] G. Huang, X. Liu, Y. Takeuchi, Lyapunov functions and global stability for age-structured HIV infection model, SIAM J. Appl. Math., 72 (2012), 25–38. doi: 10.1137/110826588
    [9] A. M. Elaiw, S. F. Alshehaiween, A. D. Hobiny, Global properties of delay-distributed HIV dynamics model including impairment of B-cell functions, Math., 7 (2019), 837. doi: 10.3390/math7090837
    [10] A. M. Elaiw, E. Kh. Elnahary, Analysis of general humoral immunity HIV dynamics model with HAART and distributed delays, Math., 7 (2019), 157. doi: 10.3390/math7020157
    [11] R. V. Culshaw, S. Ruan, G. Webb, A mathematical model of cell-to-cell spread of HIV-1 that includes a time delay, J. Math. Biol., 46 (2003), 425–444. doi: 10.1007/s00285-002-0191-5
    [12] X. Lai, X. Zou, Modelling HIV-1 virus dynamics with both virus-to-cell infection and cell-to-cell transmission, SIAM J. Appl. Math., 74 (2014), 898–917. doi: 10.1137/130930145
    [13] A. Mojaver, H. Kheiri, Mathematical analysis of a class of HIV infection models of CD$4^{+}$T-cells with combined antiretroviral therapy, Appl. Math. Comput., 259 (2015), 258–270.
    [14] D. Adak, N. Bairagi, Analysis and computation of multi-pathways and multi-delays HIV-1 infection model, Appl. Math. Model., 54 (2018) 517–536. doi: 10.1016/j.apm.2017.09.051
    [15] T. Guo, Z. Qiu, The effects of CTL immune response on HIV infection model with potent therapy, latently infected cells and cell-to-cell viral transmission, Math. Biosci. Eng., 16 (2019), 6822–6841. doi: 10.3934/mbe.2019341
    [16] H. Liu, J. F. Zhang, Dynamics of two time delays differential equation model to HIV latent infection, Phys. A., 514 (2019), 384–395. doi: 10.1016/j.physa.2018.09.087
    [17] W. Chen, N. Tuerxun, Z. Teng, The global dynamics in a wild-type and drug-resistant HIV infection model with saturated incidence, Adv. Differ. Equations, 2020 (2020), 25. doi: 10.1186/s13662-020-2497-2
    [18] K. Wang, W. Wang, Propagation of HBV with spatial dependence, Math. Biosci., 210 (2007), 78–95. doi: 10.1016/j.mbs.2007.05.004
    [19] C. Kang, H. Miao, X. Chen, J. Xu, D. Huang, Global stability of a diffusive and delayed virus dynamics model with Crowley-Martin incidence function and CTL immune response, Adv. Differ. Equations, 324 (2017).
    [20] C. C. McCluskey, Y. Yang, Global stability of a diffusive virus dynamics model with general incidence function and time delay, Nonlinear Anal. Real World Appl., 25 (2015), 64–78. doi: 10.1016/j.nonrwa.2015.03.002
    [21] H. Miao, Z. Teng, X. Abdurahman, Z. Li, Global stability of a diffusive and delayed virus infection model with general incidence function and adaptive immune response, Comput. Appl. Math., 37 (2017), 3780–3805.
    [22] A. Sigal, J. T. Kim, A. B. Balazs, E. Dekel, A. Mayo, R. Milo, et al., Cell-to-cell spread of HIV permits ongoing replication despite antiretroviral therapy, Nature, 477 (2011), 95–98. doi: 10.1038/nature10347
    [23] J. Wang, J. Yang, T. Kuniya, Dynamics of a PDE viral infection model incorporating cell-to-cell transmission, J. Math. Anal. Appl., 444 (2016), 1542–1564. doi: 10.1016/j.jmaa.2016.07.027
    [24] H. Sun, J. Wang, Dynamics of a diffusive virus model with general incidence function, cell-to-cell transmission and time delay, Comput. Math. Appl., 77 (2019), 284–301. doi: 10.1016/j.camwa.2018.09.032
    [25] Y. Gao, J. Wang, Threshold dynamics of a delayed nonlocal reaction-diffusion HIV infection model with both cell-free and cell-to-cell transmissions, J. Math. Anal. Appl., 488 (2020), 124047. doi: 10.1016/j.jmaa.2020.124047
    [26] S. Tang, Z. Teng, H. Miao, Global dynamics of a reaction-diffusion virus infection model with humoral immunity and nonlinear incidence, Comput. Math. Appl., 78 (2019), 786–806. doi: 10.1016/j.camwa.2019.03.004
    [27] Y. Luo, L. Zhang, T. Zheng, Z. Teng, Analysis of a diffusive virus infection model with humoral immunity, cell-to-cell transmission and nonlinear incidence, Phys. A, 535 (2019), 122415. doi: 10.1016/j.physa.2019.122415
    [28] A. D. AlAgha, A. M. Elaiw, Stability of a general reaction-diffusion HIV-1 dynamics model with humoral immunity, Eur. Phys. J. Plus, 134 (2019), 1–18. doi: 10.1140/epjp/i2019-12286-x
    [29] A. M. Elaiw, A. D. A. Agha, Stability of a general HIV-1 reaction-diffusion model with multiple delays and immune response, Phys. A. Stat. Mech. Appl., 536 (2019), 1–20.
    [30] W. Wang, X. Wang, K. Guo, W. Ma, Global analysis of a diffusive viral model with cell-to-cell infection and incubation period, Math. Methods Appl. Sci., 43 (2020), 5963–5978. doi: 10.1002/mma.6339
    [31] X. Ren, Y. Tian, L. Liu, X. Liu, A reaction-diffusion within-host HIV model with cell-to-cell transmission, J. Math. Biol., 76 (2018), 1831–1872. doi: 10.1007/s00285-017-1202-x
    [32] N. I. Stilianakis, J. Seydel, Modeling the T-cell dynamics and pathogenesis of HTLV-I infection, Bull. Math. Biol., 61 (1999), 935–947. doi: 10.1006/bulm.1999.0117
    [33] H. Gomez-Acevedo, M. Y. Li, Backward bifurcation in a model for HTLV-I infection of CD4$^+$ T cells, Bull. Math. Biol., 67 (2005), 101–114. doi: 10.1016/j.bulm.2004.06.004
    [34] C. Vargas-De-Leon, The complete classification for global dynamics of amodel for the persistence of HTLV-1 infection, Appl. Math. Comput., 237 (2014), 489–493.
    [35] M. Y. Li, A. G. Lim, Modelling the role of Tax expression in HTLV-1 persisence in vivo, Bull. Math. Biol., 73 (2011), 3008–3029. doi: 10.1007/s11538-011-9657-1
    [36] L. Wang, M. Y. Li, D. Kirschner, Mathematical analysis of the global dynamics of a model for HTLV-I infection and ATL progression, Math. Biosci., 179 (2002), 207–217. doi: 10.1016/S0025-5564(02)00103-7
    [37] A. G. Lim, P. K. Maini, HTLV-Iinfection: A dynamic struggle between viral persistence and host immunity, J. Theor. Biol., 352 (2014), 92–108. doi: 10.1016/j.jtbi.2014.02.022
    [38] X. Pan, Y. Chen, H. Shu, Rich dynamics in a delayed HTLV-I infection model: Stability switch, multiple stable cycles, and torus, J. Math. Anal. Appl., 479 (2019), 2214–2235. doi: 10.1016/j.jmaa.2019.07.051
    [39] H. Gomez-Acevedo, M. Y. Li, S. Jacobson, Multi-stability in a model for CTL response to HTLV-I infection and its consequences in HAM/TSP development, and prevention, Bull. Math. Biol., 72 (2010), 681–696. doi: 10.1007/s11538-009-9465-z
    [40] J. Lang, M. Y. Li, Stable and transient periodic oscillations in a mathematical model for CTL response to HTLV-I infection, J. Math. Biol., 65 (2012), 181–199. doi: 10.1007/s00285-011-0455-z
    [41] M. Y. Li, H. Shu, Multiple stable periodic oscillations in a mathematical model of CTL response to HTLV-I infection, Bull. Math. Biol., 73 (2011), 1774–1793. doi: 10.1007/s11538-010-9591-7
    [42] L. Wang, Z. Liu, Y. Li, D. Xu, Complete dynamical analysis for a nonlinear HTLV-I infection model with distributed delay, CTL response and immune impairment, Discrete Cont. Dyn. Syst. Ser. B, 25 (2020), 917–933.
    [43] Y. Wang, J. Liu, J. M. Heffernan, Viral dynamics of an HTLV-I infection model with intracellular delay and CTL immune response delay, J. Math. Anal. Appl., 459 (2018), 506–527. doi: 10.1016/j.jmaa.2017.10.027
    [44] F. Li, W. Ma, Dynamics analysis of an HTLV-1 infection model with mitotic division of actively infected cells and delayed CTL immune response, Math. Methods Appl. Sci., 41 (2018), 3000–3017. doi: 10.1002/mma.4797
    [45] S. Li, Y. Zhou, Backward bifurcation of an HTLV-I model with immune response, Discrete Cont. Dyn. Syst. Ser. B, 21 (2016), 863–881. doi: 10.3934/dcdsb.2016.21.863
    [46] W. Wang, W. Ma, Global dynamics of a reaction and diffusion model for an HTLV-I infection with mitotic division of actively infected cells, J. Appl. Anal. Comput., 7 (2017), 899–930.
    [47] C. Casoli, E. Pilotti, U. Bertazzoni, Molecular and cellular interactions of HIV-1/HTLV coinfection and impact on AIDS progression, AIDS Rev., 9 (2007), 140–149.
    [48] M. T. Silva, O. M. Espíndola, A. C. B. Leite, A. Araújo, Neurological aspects of HIV/human T lymphotropic virus coinfection, AIDS Rev., 11 (2009), 71–78.
    [49] C. Brites, J. Sampalo, A. Oliveira, HIV/human T-cell lymphotropic virus coinfection revisited: impact on AIDS progression, AIDS Rev., 11 (2009), 8–16.
    [50] C. Isache, M. Sands, N. Guzman, D. Figueroa, HTLV-1 and HIV-1 co-infection: A case report and review of the literature, IDCases, 4 (2016), 53–55. doi: 10.1016/j.idcr.2016.03.002
    [51] M. A. Beilke, K. P. Theall, M. O'Brien, J. L. Clayton, S. M. Benjamin, E. L. Winsor, et al., Clinical outcomes and disease progression among patients coinfected with HIV and human T lymphotropic virus types 1 and 2, Clin. Infect. Dis., 39 (2004), 256–263. doi: 10.1086/422146
    [52] A. M. Elaiw, N. H. AlShamrani, Analysis of a within-host HIV/HTLV-I co-infection model with immunity, Virus Res., 295 (2020), 198204.
    [53] A. M. Elaiw, N. H. AlShamrani, HTLV/HIV dual infection: Modeling and analysis, Mathematics, 9 (2021), 51.
    [54] Z. Wang, Z. Guo, H. Peng, Dynamical behavior of a new oncolytic virotherapy model based on gene variation, Discrete Contin. Dyn. Syst. Ser. S, 10 (2017), 1079–1093.
    [55] Z. Xu, Y. Xu, Stability of a CD$4^{+}$ T cell viral infection model with diffusion, Int. J. Biomath., 11 (2018), 1850071. doi: 10.1142/S1793524518500717
    [56] Y. Zhang, Z. Xu, Dynamics of a diffusive HBV model with delayed Beddington-DeAngelis response, Nonlinear Anal. Real World Appl., 15 (2014), 118–139. doi: 10.1016/j.nonrwa.2013.06.005
    [57] H. L. Smith, Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems, American Mathematical Society, USA, 1995.
    [58] M. H. Protter, H. F. Weinberger, Maximum Principles in Differential Equations, Prentice-Hall, New York, 1967.
    [59] D. Henry, Geometric Theory of Semilinear Parabolic Equations, Springer-Verlag, New York, 1993.
    [60] A. M. Elaiw, A. D. AlAgha, Global dynamics of reaction-diffusion oncolytic M1 virotherapy with immune response, Appl. Math. Comput., 367 (2020), 124758.
    [61] A. M. Elaiw, A. D. AlAgha, Analysis of a delayed and diffusive oncolytic M1 virotherapy model with immune response, Nonlinear Anal. Real World Appl., 55 (2020), 103116. doi: 10.1016/j.nonrwa.2020.103116
    [62] E. A. Barbashin, Introduction to the Theory of Stability, Springer-Verlag, Groningen, 1970.
    [63] J. P. LaSalle, The Stability of Dynamical Systems, SIAM, Philadelphia, 1976.
    [64] A. M. Lyapunov, The general problem of the stability of motion, Int. J. Control, 55 (1992), 531–773. doi: 10.1080/00207179208934253
    [65] A. S. Perelson, D. E. Kirschner, R. D. boer, Dynamics of HIV Infection of CD$4^{+}$T cells, Math. Biosci., 114 (1993), 81–125. doi: 10.1016/0025-5564(93)90043-A
    [66] H. Mohri, S. Bonhoeffer, S. Monard, A. S. Perelson, D. D. Ho, Rapid turnover of T lymphocytes in SIV-infected rhesus macaques, Science, 279 (1998), 1223–1227. doi: 10.1126/science.279.5354.1223
    [67] D. Wodarz, Mathematical models of immune effector responses to viral infections: Virus control versus the development of pathology, J. Comput. Appl. Math., 184 (2005), 301–319. doi: 10.1016/j.cam.2004.08.016
    [68] C. Qin, Y. Chen, X. Wang, Global dynamics of a delayed diffusive virus infection model with cell-mediated immunity and cell-to-cell transmission, Math. Biosci. Eng., 17 (2020), 4678–4705. doi: 10.3934/mbe.2020257
    [69] L. Rong, A. S. Perelson, Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy, PLoS Comput. Biol., 5 (2009), e1000533. doi: 10.1371/journal.pcbi.1000533
    [70] S. K. Sahani, Yashi, Effects of eclipse phase and delay on the dynamics of HIV infection, J. Biol. Syst., 26 (2018), 421–454. doi: 10.1142/S0218339018500195
    [71] J. Liu, Q. Zhang, C. Tian, Effect of time delay on spatial patterns in a airal infection model with diffusion, Math. Model. Anal., 21 (2016), 143–158. doi: 10.3846/13926292.2016.1137503
    [72] X. Wang, X. Tang, Z. Wang, X. Li, Global dynamics of a diffusive viral infection model with general incidence function and distributed delays, Ric. Mat., 69 (2020), 683–702. doi: 10.1007/s11587-020-00481-0
    [73] A. Vandormael, F. Rego, S. Danaviah, L. C. J. Alcantara, D. R. Boulware, T. Oliveira, CD$4^{+}$ T-cell count may not be a useful strategy to monitor antiretroviral therapy response in HTLV-1/HIV co-infected patients, Curr. HIV Res., 15 (2017), 225–231.
  • Reader Comments
  • © 2021 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(2271) PDF downloads(88) Cited by(1)

Article outline

Figures and Tables

Figures(5)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog