Within-host model of an HTLV-1 infection of CD4+ T-cells incorporating an adult T-cell leukemia development: stability analysis and optimal control by prevention and two treatments
Authors
S. Daengkongkho
- Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.
R. Viriyapong
- Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand.
- Research Center for Academic Excellence in Mathematics, Naresuan University, Phitsanulok, 65000, Thailand.
Abstract
Human T-cell leukemia/lymphotropic virus type 1 (HTLV-1) is a pathogenic retrovirus that infects CD4+ T-cells. HTLV-1 associates with various diseases including the development of adult T-cell leukemia (ATL) and myelopathy/tropical spastic paraparesis (HAM/TSP). Currently, there is no cure or neutralizing vaccine for HTLV-1 infection. To better understanding of HTLV-1 infection dynamics for CD4+ T-cells, in this study we propose a within-host model with six classes of cells includes uninfected, latently infected, actively infected CD4+ T-cells, leukemia cells, HTLV-1-specific CD8+ T-cells or CTLs, and free virus. Mathematical analysis is performed in detail starting with non-negativity and the boundedness of the solutions are verified, three equilibrium points (infection-free, immune-free and chronic infection) are computed, the model basic reproduction number is calculated, and its sensitivity is performed. When the basic reproduction number is less than one, the infection can be eliminated, otherwise the infection persists. Both local and global stability of all equilibrium points are analyzed, where we obtain that each equilibrium point is stable under some specific conditions. Next, an optimal control problem is applied into the model to investigate the optimal strategy of three considered controls which are preventive control, treatment effort for leukemia cells and antivirus drugs for HTLV-1. Our numerical results demonstrates that preventive control has shown to reduce the HTLV-1 infection of CD4+ T-cells, the treatment control for leukemia cells can reduce the leukemia cells, and the antivirus drugs for HTLV-1 can reduce the actively infected CD4+ T-cells and free virus. However, a combination of all three controls gives the most effective results for reducing the overall HTLV-1 infection of CD4+ T-cells.
Share and Cite
ISRP Style
S. Daengkongkho, R. Viriyapong, Within-host model of an HTLV-1 infection of CD4+ T-cells incorporating an adult T-cell leukemia development: stability analysis and optimal control by prevention and two treatments, Journal of Mathematics and Computer Science, 39 (2025), no. 2, 160--191
AMA Style
Daengkongkho S., Viriyapong R., Within-host model of an HTLV-1 infection of CD4+ T-cells incorporating an adult T-cell leukemia development: stability analysis and optimal control by prevention and two treatments. J Math Comput SCI-JM. (2025); 39(2):160--191
Chicago/Turabian Style
Daengkongkho, S., Viriyapong, R.. "Within-host model of an HTLV-1 infection of CD4+ T-cells incorporating an adult T-cell leukemia development: stability analysis and optimal control by prevention and two treatments." Journal of Mathematics and Computer Science, 39, no. 2 (2025): 160--191
Keywords
- HTLV-1 infection
- immune response
- global stability
- optimal control problem
- numerical simulation
MSC
- 34D05
- 34D20
- 34D23
- 49J15
- 92B05
References
-
[1]
B. Asquith, Y. Zhang, A. J. Mosley, C. M. de Lara, D. L. Wallace, A. Worth, L. Kaftantzi, K. Meekings, G. E. Griffin, Y. Tanaka, D. F. Tough, P. C. Beverley, G. P. Taylor, D. C. Macallan, C. R. M. Bangham, In vivo T lymphocyte dynamics in humans and the impact of human T-lymphotropic virus 1 infection, Proc. Natl. Acad. Sci., 104 (2007), 8035–8040
-
[2]
S. Bera, S. Khajanchi, T. K. Kar, Stochastic persistence, extinction and stationary distribution in HTLV-I infection model with CTL immune response, Qual. Theory Dyn. Syst., 23 (2024), 37 pages
-
[3]
S. Bera, S. Khajanchi, T. K. Roy, Dynamics of an HTLV-I infection model with delayed CTLs immune response, Appl. Math. Comput., 430 (2022), 31 pages
-
[4]
S. Bera, S. Khajanchi, T. K. Roy, Stability analysis of fuzzy HTLV-I infection model: a dynamic approach, J. Appl. Math. Comput., 69 (2023), 171–199
-
[5]
R. Butler, M. Nisan, Effects of no feedback, task-related comments, and grades on intrinsic motivation and performance, J. Educ. Psychol., 78 (1986), 210–216
-
[6]
Global dynamics of a mathematical model for HTLV-I infection of CD4+ T-cells, L. Cai, X. Li, M. Ghosh, Appl. Math. Model., 35 (2011), 3587–3595
-
[7]
D. S. Callaway, A. S. Perelson, HIV-1 infection and low steady state viral loads, Bull. Math. Biol., 64 (2002), 29–64
-
[8]
C. Castillo-Chavez, Z. Feng, W. Huang, On the computation of R0 and its role on global stability, Springer, New York, 125 (2002), 229–250
-
[9]
S. A. Cooper, M. S. van der Loeff, G. P. Taylor, The neurology of HTLV-1 infection, Pract. Neurol., 9 (2009), 16–26
-
[10]
A. Das, K. Dehingia, H. K. Sarmah, K. Hosseini, An optimally controlled chemotherapy treatment for cancer eradication, Int. J. Model. Simul., 44 (2024), 44–59
-
[11]
L. Einsiedel, F. Chiong, H. Jersmann, G. P. Taylor, Human T-cell leukaemia virus type 1 associated pulmonary disease: clinical and pathological features of an under-recognised complication of HTLV-1 infection, Retrovirology, 18 (2021), 13 pages
-
[12]
A. M. Elaiw, N. H. AlShamrani, Stability of HTLV/HIV dual infection model with mitosis and latency, Math. Biosci. Eng., 18 (2021), 1077–1120
-
[13]
N. Eshima, M. Tabata, T. Okada, S. Karukaya, Population dynamics of HTLV-I infection: A discrete-time mathematical epidemic model approach, Math. Med. Biol., 20 (2003), 29–45
-
[14]
M. Freedman, L. Leach, E. Kaplan, G. Winocur, K. I. Shulman, D. C. Delis, Clock drawing: A neuropsychological analysis, Oxford University Press, New York (1994)
-
[15]
A. Gessain, O. Cassar, Epidemiological aspects and world distribution of HTLV-1 infection, Front. Microbiol., 3 (2012), 23 pages
-
[16]
H. Gómez-Acevedo, M. Y. Li, S. Jacobson, Multistability in a model for CTL response to HTLV-I infection and its implications to HAM/TSP development and prevention, Bull. Math. Biol., 72 (2010), 681–696
-
[17]
B. Hanchard, W. N. Gibbs, W. Lofters, M. Campbell, E. Williams, N. Williams, E. Jaffe, B. Cranston, L. D. Panchoosingh, W. A. Blattner, A Human retrovirology: HTLV, Raven Press, New York (1990)
-
[18]
, Healthdirect, HTLV-1 infection, Available online at: https://www.healthdirect.gov.au/htlv-1-infection/(accessed on January 5. 2023), (),
-
[19]
S. Hino, Establishment of the mild-borne transmission as a key factor for the peculiar endemicity of human T-lymphotropic virus type 1 (HTLV-1): the ATL Prevention Program Nagasaki, Proc. Jpn. Acad. Ser. B, 87 (2011), 152–166
-
[20]
R. Jan, N. N. A. Razak, S. Alyobi, Z. Khan, K. Hosseini, C. Park, S. Salahshour, S. Paokanta, Fractional dynamics of chronic lymphocytic leukemia with the effect of chemoimmunotherapy treatment, Fractals, 32 (2024), 16 pages
-
[21]
P. Katri, S. Ruan, Dynamics of human T-cell lymphotropic virus I (HTLV-I) infection of CD4+ T-cells, C. R. Biol., 327 (2004), 1009–1016
-
[22]
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. Simul., 180 (2021), 354–378
-
[23]
S. Khajanchi, K. Sarkar, Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India, Chaos, 30 (2020), 16 pages
-
[24]
S. Khajanchi, K. Sarkar, S. Banerjee, Modeling the dynamics of COVID-19 pandemic with implementation of intervention strategies, Eur. Phys. J. Plus, 137 (2022), 22 pages
-
[25]
S. Khajanchi, K. Sarkar, J. Mondal, K. S. Nisar, S. F. Abdelwahab, Mathematical modeling of the COVID-19 pandemic with intervention strategies, Results Phys., 25 (2021), 16 pages
-
[26]
J. P. LaSalle, The stability of dynamical systems, SIAM, Philadelphia (1976)
-
[27]
M. Y. Li, A. G. Lim, Modelling the role of tax expression in HTLV-I persistence in vivo, Bull. Math. Biol., 73 (2011), 3008–3029
-
[28]
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
-
[29]
Y. Li, J. S. Muldowney, On Bendixson’ s Criterion, J. Differ. Equ., 106 (1993), 27–39
-
[30]
M. Y. Li, J. S. Muldowney, A geometric approach to global-stability problems, SIAM J. Math. Anal., 27 (1996), 1070–1083
-
[31]
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
-
[32]
M. Y. Li, H. Shu, Global dynamics of a mathematical model for HTLV-I infection of CD4+ T- cells with delayed CTL response, Nonlinear Anal. Real World Appl., 13 (2012), 1080–1092
-
[33]
A. G. Lim, P. K. Maini, HTLV-I infection: a dynamic struggle between viral persistence and host immunity, J. Theor. Biol., 352 (2014), 92–108
-
[34]
W. R. Mbogo, L. S. Luboobi, J. W. Odhiambo, Stochastic model for in-host HIV dynamics with therapeutic intervention, Int. Sch. Res. Not. Biomath., 2013 (2013), 11 pages
-
[35]
J. Mondal, S. Khajanchi, Mathematical modeling and optimal intervention strategies of the COVID-19 outbreak, Nonlinear Dyn., 109 (2022), 177–202
-
[36]
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), 23 pages
-
[37]
B. J. Nath, K. Sadri, H. K. Sarmah, K. Hosseini, An optimal combination of antiretroviral treatment and immunotherapy for controlling HIV infection, Math. Comput. Simul., 217 (2024), 226–243
-
[38]
M. A. Nowak, C. R. M. Bangham, Population dynamics of immune responses to persistent viruses, Science, 272 (1996), 74–79
-
[39]
A. S. Perelson, D. E. Kirschner, R. De Boer, Dynamics of HIV infection of CD4+ T cells, Math. Biosci., 114 (1993), 81–125
-
[40]
L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze, E. F. Mishchenko, The mathematical theory of optimal processes, CRC Press, London (1987)
-
[41]
R. K. Rai, S. Khajanchi, P. K. Tiwari, E. Venturino, A. K. Misra, Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India, J. Appl. Math. Comput., 68 (2022), 19–44
-
[42]
P. Samui, J. Mondal, S. Khajanchi, A mathematical model for COVID-19 transmission dynamics with a case study of India, Chaos Solitons Fractals, 140 (2020), 11 pages
-
[43]
K. Sarkar, S. Khajanchi, J. J. Nieto, Modeling and forecasting the COVID-19 pandemic in India, Chaos Solitons Fractals, 139 (2020), 16 pages
-
[44]
C. Song, R. Xu, Mathematical analysis of an HTLV-I infection model with the mitosis of CD4+ T cells and delayed CTL immune response, Nonlinear Anal. Model. Control, 26 (2021), 1–20
-
[45]
N. I. Stilianakis, J. Seydel, Modeling the T-cell dynamics and pathogenesis of HTLV-I infection, Bull. Math. Biol., 61 (1999), 935–947
-
[46]
P. K. Tiwari, R. K. Rai, S. Khajanchi, R. K. Gupta, A. K. Misra, Dynamics of coronavirus pandemic: effects of community awareness and global information campaigns, Eur. Phys. J. Plus, 136 (2021), 23 pages
-
[47]
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
-
[48]
R. Viriyapong, S. Wuttigan, Stability and optimal control of human T-cell lymphotropic virus type I (HTLV-I) infection of CD4+ T-cells which leads to leukemia model with prevention and treatment, Thai J. Math., 21 (2021), 491–511
-
[49]
, WHO (World Health Organization), Human T-lymphotropic virus type 1, Available online at: https://www.who.int/news-room/fact-sheets/detail/human-t-lymphotropic-virus-type-1/(accessed on December 18. 2022), (),