Research article Special Issues

Dynamic analysis and optimal control of a fractional order HIV/HTLV co-infection model with HIV-specific antibody immune response

  • Received: 28 December 2023 Revised: 23 February 2024 Accepted: 27 February 2024 Published: 07 March 2024
  • MSC : 26A33, 92B05

  • In this paper, a fractional order HIV/HTLV co-infection model with HIV-specific antibody immune response is established. Two cases are considered: constant control and optimal control. For the constant control system, the existence and uniqueness of the positive solutions are proved, and then the sufficient conditions for the existence and stability of five equilibriums are obtained. For the second case, the Pontryagin's Maximum Principle is used to analyze the optimal control, and the formula of the optimal solution are derived. After that, some numerical simulations are performed to validate the theoretical prediction. Numerical simulations indicate that in the case of HIV/HTLV co-infection, the concentration of $CD4^{+}T$ cells is no longer suitable as an effective reference data for understanding the development process of the disease. On the contrary, the number of HIV virus particles should be used as an important indicator for reference.

    Citation: Ruiqing Shi, Yihong Zhang. Dynamic analysis and optimal control of a fractional order HIV/HTLV co-infection model with HIV-specific antibody immune response[J]. AIMS Mathematics, 2024, 9(4): 9455-9493. doi: 10.3934/math.2024462

    Related Papers:

  • In this paper, a fractional order HIV/HTLV co-infection model with HIV-specific antibody immune response is established. Two cases are considered: constant control and optimal control. For the constant control system, the existence and uniqueness of the positive solutions are proved, and then the sufficient conditions for the existence and stability of five equilibriums are obtained. For the second case, the Pontryagin's Maximum Principle is used to analyze the optimal control, and the formula of the optimal solution are derived. After that, some numerical simulations are performed to validate the theoretical prediction. Numerical simulations indicate that in the case of HIV/HTLV co-infection, the concentration of $CD4^{+}T$ cells is no longer suitable as an effective reference data for understanding the development process of the disease. On the contrary, the number of HIV virus particles should be used as an important indicator for reference.



    加载中


    [1] S. M. Salman, Memory and media coverage effect on an HIV/AIDS epidemic model with treatment, J. Comput. Appl. Math., 385 (2021), 113203. http://dx.doi.org/10.1016/j.cam.2020.113203 doi: 10.1016/j.cam.2020.113203
    [2] HIV, World Health Organization, 2022.
    [3] A. S. Perelson, Modeling the interaction of the immune system with HIV, In: Mathematical and statistical approaches to AIDS epidemiology, Berlin, Heidelberg: Springer, 1989. http://dx.doi.org/10.1007/978-3-642-93454-4_17
    [4] H. Ye, Modeling and analyzing of the dynamics of HIV infections based on fractional differential equations, Doctoral thesis, Donghua University, 2009.
    [5] R. Xu, C. Song, Dynamics of an HIV infection model with virus diffusion and latently infected cell activation, Nonlinear Anal. Real World Appl., 67 (2022), 103618. https://doi.org/10.1016/j.nonrwa.2022.103618 doi: 10.1016/j.nonrwa.2022.103618
    [6] P. Wu, S. Zheng, Z. He, Evolution dynamics of a time-delayed reaction-diffusion HIV latent infection model with two strains and periodic therapies, Nonlinear Anal. Real World Appl., 67 (2022), 103559. https://doi.org/10.1016/j.nonrwa.2022.103559 doi: 10.1016/j.nonrwa.2022.103559
    [7] P. Wu, H. Zhao, Mathematical analysis of an age-structured HIV/AIDS epidemic model with HAART and spatial diffusion, Nonlinear Anal. Real World Appl., 60 (2021), 103289. https://doi.org/10.1016/j.nonrwa.2021.103289 doi: 10.1016/j.nonrwa.2021.103289
    [8] B. J. Nath, K. Dehingia, K. Sadri, H. K. Sarmah, K. Hosseini, C. Park, Optimal control of combined antiretroviral therapies in an HIV infection model with cure rate and fusion effect, Int. J. Biomath, 16 (2023), 2250062. https://doi.org/10.1142/S1793524522500620 doi: 10.1142/S1793524522500620
    [9] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, Stationary distribution and extinction of a stochastic HIV-1 infection model with distributed delay and logistic growth, J. Nonlinear Sci., 30 (2020), 369–395. https://doi.org/10.1007/s00332-019-09576-x doi: 10.1007/s00332-019-09576-x
    [10] K. Qi, D. Jiang, The impact of virus carrier screening and actively seeking treatment on dynamical behavior of a stochastic HIV/AIDS infection model, Appl. Math. Model., 85 (2020), 378–404. https://doi.org/10.1016/j.apm.2020.03.027 doi: 10.1016/j.apm.2020.03.027
    [11] Q. Liu, Dynamics of a stochastic SICA epidemic model for HIV transmission with higher-order perturbation, Stoch. Anal. Appl., 40 (2022), 209–235. https://doi.org/10.1080/07362994.2021.1898979 doi: 10.1080/07362994.2021.1898979
    [12] J. Ren, Q. Zhang, X. Li, F. Cao, M. Ye, A stochastic age-structured HIV/AIDS model based on parameters estimation and its numerical calculation, Math. Comput. Simulat., 190 (2021), 159–180. https://doi.org/10.1016/j.matcom.2021.04.024 doi: 10.1016/j.matcom.2021.04.024
    [13] Y. Tan, Y. Cai, X. Sun, K. Wang, R. Yao, W. Wang, et al., A stochastic SICA model for HIV/AIDS transmission, Chaos Soliton Fract., 165 (2022), 112768. https://doi.org/10.1016/j.chaos.2022.112768 doi: 10.1016/j.chaos.2022.112768
    [14] F. Rao, J. Luo, Stochastic effects on an HIV/AIDS infection model with incomplete diagnosis, Chaos Soliton Fract., 152 (2021), 111344. https://doi.org/10.1016/j.chaos.2021.111344 doi: 10.1016/j.chaos.2021.111344
    [15] R. Shi, T. Lu, C. Wang, Dynamic analysis of a fractional-order model for HIV with drug-resistance and CTL immune response, Math. Comput. Simulat., 188 (2021), 509–536. https://doi.org/10.1016/j.matcom.2021.04.022 doi: 10.1016/j.matcom.2021.04.022
    [16] H. Singh, Analysis of drug treatment of the fractional HIV infection model of $CD4^{+}T$-cells, Chaos Soliton Fract., 146 (2021), 11068. https://doi.org/10.1016/j.chaos.2021.110868 doi: 10.1016/j.chaos.2021.110868
    [17] Y. Zhao, E. E. Elattar, M. A. Khan, Fatmawati, M. Asiri, P. Sunthrayuth, The dynamics of the HIV/AIDS infection in the framework of piecewise fractional differential equation, Results Phys., 40 (2022), 105842. https://doi.org/10.1016/j.rinp.2022.105842 doi: 10.1016/j.rinp.2022.105842
    [18] M. Jafari, H. Kheiri, Free terminal time optimal control of a fractional-order model for the HIV/AIDS epidemic, Int. J. Biomath., 15 (2022), 2250022. https://doi.org/10.1142/S179352452250022X doi: 10.1142/S179352452250022X
    [19] B. Asquith, C. Bangham, Quantifying HTLV-I dynamics, Immunol. Cell Biol., 85 (2007), 280–286. https://doi.org/10.1038/sj.icb.7100050 doi: 10.1038/sj.icb.7100050
    [20] L. M. Mansky, In vivo analysis of human T-cell leukemia virus type Ⅰ reverse transcription accuracy, J. Virol., 74 (2000), 9525–9531. https://doi.org/10.1128/JVI.74.20.9525-9531.2000 doi: 10.1128/JVI.74.20.9525-9531.2000
    [21] Q. Kai, D. Jiang, Threshold behavior in a stochastic HTLV-I infection model with CTL immune response and regime switching, Math. Method. Appl. Sci., 41 (2018), 6866–6882. https://doi.org/10.1002/mma.5198 doi: 10.1002/mma.5198
    [22] Z. Shi, D. Jiang, Dynamical behaviors of a stochastic HTLV-I infection model with general infection form and Ornstein-Uhlenbeck process, Chaos Soliton Fract., 165 (2022), 112789. https://doi.org/10.1016/j.chaos.2022.112789 doi: 10.1016/j.chaos.2022.112789
    [23] S. Bera, S. Khajanchi, T. K. Roy, Dynamics of an HTLV-I infection model with delayed CTLs immune response, Appl. Math. Comput., 430 (2022), 127206. https://doi.org/10.1016/j.amc.2022.127206 doi: 10.1016/j.amc.2022.127206
    [24] A. M. Elaiw, A. S. Shflot, A. D. Hobiny, Global stability of a general HTLV-I infection model with Cytotoxic T-Lymphocyte immune response and mitotic transmission, Alexandria Eng., 67 (2023), 77–91. https://doi.org/10.1016/j.aej.2022.08.021 doi: 10.1016/j.aej.2022.08.021
    [25] S. Khajanchi, S. Bera, T. K. Roy, Mathematical analysis of the global dynamics of a HTLV-I infection model, considering the role of cytotoxic T-lymphocytes, Math. Comput. Simulat., 180 (2021), 354–378. https://doi.org/10.1016/j.matcom.2020.09.009 doi: 10.1016/j.matcom.2020.09.009
    [26] N. Kobayashi, Y. Hamamoto, N. Yamamoto, Production of tumor necrosis factors by human T cell lines infected with HTLV-1 may cause their high susceptibility to human immunodeficiency virus infection, Med. Microbiol. Immunol., 179 (1990), 115–122. https://doi.org/10.1007/BF00198532 doi: 10.1007/BF00198532
    [27] C. D. Mendoza, E. Caballero, A. Aguilera, R. Benito, D. Maciá, J. García-Costa, et al., HIV co-infection in HTLV-1 carriers in Spain, Virus Res., 266 (2019), 48–51. https://doi.org/10.1016/j.virusres.2019.04.004 doi: 10.1016/j.virusres.2019.04.004
    [28] M. A. Alshaikh, N. H. Alshamrani, A. M. Elaiw, Stability of HIV/HTLV co-infection model with effective HIV-specific antibody immune response, Results Phys., 27 (2021), 104448. https://doi.org/10.1016/j.rinp.2021.104448 doi: 10.1016/j.rinp.2021.104448
    [29] A. M. Elaiw, N. H. Alshamrani, Analysis of a within-host HIV/HTLV-I co-infection model with immunity, Virus Res., 295 (2021), 198204. https://doi.org/10.1016/j.virusres.2020.198204 doi: 10.1016/j.virusres.2020.198204
    [30] A. M. Elaiw, N. H. Alshamrani, E. Dahy, A. A. Abdellatif, Stability of within host HTLV-I/HIV-1 co-infection in the presence of macrophages, Int. J. Biomath, 16 (2023), 2250066. https://doi.org/10.1142/S1793524522500668 doi: 10.1142/S1793524522500668
    [31] Z. Guo, H. Huo, H. Xiang, Optimal control of TB transmission based on an age structured HIV-TB co-infection model, J. Franklin Inst., 359 (2022), 4116–4137. https://doi.org/10.1016/j.jfranklin.2022.04.005 doi: 10.1016/j.jfranklin.2022.04.005
    [32] A. Mallela, S. Lenhart, N. K. Vaidya, HIV-TB co-infection treatment: Modeling and optimal control theory perspectives, J. Comput. Appl. Math., 307 (2016), 143–161. https://doi.org/10.1016/j.cam.2016.02.051 doi: 10.1016/j.cam.2016.02.051
    [33] Tanvi, R. Aggarwal, Stability analysis of a delayed HIV-TB co-infection model in resource limitation settings, Chaos Soliton Fract., 140 (2020), 110138. https://doi.org/10.1016/j.chaos.2020.110138 doi: 10.1016/j.chaos.2020.110138
    [34] L. Zhang, M. U. Rahman, M. Arfan, A. Ali, Investigation of mathematical model of transmission co-infection TB in HIV community with a non-singular kernel, Results Phys., 28 (2021), 104559. https://doi.org/10.1016/j.rinp.2021.104559 doi: 10.1016/j.rinp.2021.104559
    [35] N. H. Shah, N. Sheoran, Y. Shah, Dynamics of HIV-TB co-infection model, Axioms, 9 (2020), 29. https://doi.org/10.3390/axioms9010029 doi: 10.3390/axioms9010029
    [36] I. Ahmed, E. F. D. Goufo, A. Yusuf, P. Kumam, K. Nonlaopon, An epidemic prediction from analysis of a combined HIV-COVID-19 co-infection model via ABC-fractional operator, Alexandria Eng. J., 60 (2021), 2979–2995. https://doi.org/10.1016/j.aej.2021.01.041 doi: 10.1016/j.aej.2021.01.041
    [37] N. Ringa, M. L. Diagne, H. Rwezaura, A. Omame, S. Y. Tchoumi, J. M. Tchuenche, HIV and COVID-19 co-infection: A mathematical model and optimal control, Inform. Med. Unlocked, 31 (2022), 100978. https://doi.org/10.1016/j.imu.2022.100978 doi: 10.1016/j.imu.2022.100978
    [38] A. Omame, M. E. Isah, M. Abbas, A. H. Abdel-Aty, C. P. Onyenegecha, A fractional order model for Dual Variants of COVID-19 and HIV co-infection via Atangana-Baleanu derivative, Alexandria Eng. J., 61 (2022), 9715–9731. https://doi.org/10.1016/j.aej.2022.03.013 doi: 10.1016/j.aej.2022.03.013
    [39] V. Lakshmikantham, A. S. Vatsala, Basic theory of fractional differential equations, Nonlinear Anal. Theor., 69 (2008), 2677–2682. https://doi.org/10.1016/j.na.2007.08.042 doi: 10.1016/j.na.2007.08.042
    [40] I. Podlubny, Fractional differential equations: An introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications, San Diego: Academic Press, 1999.
    [41] K. Hattaf, A new generalized definition of fractional derivative with non-singular kernel, Computation, 8 (2020), 49. https://doi.org/10.3390/computation8020049 doi: 10.3390/computation8020049
    [42] M. Bachraoui, K. Hattaf, N. Yousfi, Analysis of a fractional reaction-diffusion HBV model with cure of infected cells, Discrete Dyn. Nat. Soc., 2020 (2020), 3140275. https://doi.org/10.1155/2020/3140275 doi: 10.1155/2020/3140275
    [43] C. Huang, L. Cai, J. Cao, Linear control for synchronization of a fractional-order time-delayed chaotic financial system, Chaos Soliton Fract., 113 (2018), 326–332. https://doi.org/10.1016/j.chaos.2018.05.022 doi: 10.1016/j.chaos.2018.05.022
    [44] R. Rakkiyappan, G. Velmurugan, J. Cao, Stability analysis of fractional-order complex-valued neural networks with time delays, Chaos Soliton Fract., 78 (2015), 297–316. https://doi.org/10.1016/j.chaos.2015.08.003 doi: 10.1016/j.chaos.2015.08.003
    [45] H. Li, Y. Shen, Y. Han, J. Dong, J. Li, Determining Lyapunov exponents of fractional-order systems: A general method based on memory principle, Chaos Soliton Fract., 168 (2023), 113167. https://doi.org/10.1016/j.chaos.2023.113167 doi: 10.1016/j.chaos.2023.113167
    [46] Q. Gao, J. Cai, Y. Liu, Y. Chen, L. Shi, W. Xu, Power mapping-based stability analysis and order adjustment control for fractional-order multiple delayed systems, ISA Trans., 138 (2023), 10–19. https://doi.org/10.1016/j.isatra.2023.02.019 doi: 10.1016/j.isatra.2023.02.019
    [47] C. Pinto, A. Carvalho, The role of synaptic transmission in a HIV model with memory, Appl. Math. Comput., 292 (2017), 76–95. https://doi.org/10.1016/j.amc.2016.07.031 doi: 10.1016/j.amc.2016.07.031
    [48] T. Sardar, S. Rana, S. Bhattacharya, K. Al-Khaled, J. Chattopadhyay, A generic model for a single strain mosquito-transmitted disease with memory on the host and the vector, Math. Biosci., 263 (2015), 18–36. https://doi.org/10.1016/j.mbs.2015.01.009 doi: 10.1016/j.mbs.2015.01.009
    [49] K. Diethelm, Monotonicity of functions and sign changes of their Caputo derivatives, Fract. Calc. Appl. Anal., 19 (2016), 561–566. https://doi.org/10.1515/FCA-2016-0029 doi: 10.1515/FCA-2016-0029
    [50] C. Kou, Y. Yan, J. Liu, Stability analysis for fractional differential equations and their applications in the models of HIV-1 infection, Comput. Model. Eng. Sci., 39 (2009), 301–317.
    [51] W. Lin, Global existence theory and chaos control of fractional differential equations, J. Math. Anal. Appl., 332 (2007), 709–726. https://doi.org/10.1016/j.jmaa.2006.10.040 doi: 10.1016/j.jmaa.2006.10.040
    [52] P. Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental systems 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
    [53] E. Ahmed, A. M. A. El-Sayed, H. A. A. El-Saka, On some Routh-Hurwitz conditions for fractional order differential equations and their applications in Lorenz, Rssler, Chua and Chen systems, Phys. Lett. A, 358 (2006), 1–4. https://doi.org/10.1016/j.physleta.2006.04.087 doi: 10.1016/j.physleta.2006.04.087
    [54] J. P. LaSalle, Stability theory for ordinary differential equations, J. Differ. Equ., 4 (1968), 57–65. https://doi.org/10.1016/0022-0396(68)90048-X doi: 10.1016/0022-0396(68)90048-X
    [55] R. Shi, T. Lu, Dynamic analysis and optimal control of a fractional order model for hand-foot-mouth Disease, J. Appl. Math. Comput., 64 (2020), 565–590. https://doi.org/10.1007/s12190-020-01369-w doi: 10.1007/s12190-020-01369-w
    [56] E. Roxin, Differential equations: Classical to controlled, Am. Math. Mon., 92 (1985), 223–225. https://doi.org/10.1080/00029890.1985.11971586 doi: 10.1080/00029890.1985.11971586
    [57] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gramkrelidze, E. F. Mischenko, The mathematical theory of optimal processes, New York: Interscience Publishers, 1962.
    [58] N. H. Sweilam, S. M. Al-Mekhlafi, On the optimal control for fractional multi-strain TB model, Optim. Contr. Appl. Met., 37 (2016), 1355–1374. https://doi.org/10.1002/oca.2247 doi: 10.1002/oca.2247
    [59] L. Zhang, HIV viral load and $CD4^+T$ lymphocyte count in HIV-1/HTLV-1 co-infected patients, Foreign Med. Sci. Sect. Virol., 5 (1998), 27–29.
  • Reader Comments
  • © 2024 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(846) PDF downloads(84) Cited by(1)

Article outline

Figures and Tables

Figures(11)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog