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Local and global stability of a fractional viral infection model with two routes of propagation, cure rate and non-lytic humoral immunity

Year 2024, Volume: 4 Issue: 5-Special Issue: ICAME'24, 94 - 115, 31.12.2024
https://doi.org/10.53391/mmnsa.1517325

Abstract

A fractional viral model is proposed in this work, as fractional-order calculus is considered more suitable than integer-order calculus for modeling virological systems with inherent memory and long-range interactions. The model incorporates virus-to-cell infection, cell-to-cell transmission, cure rate, and humoral immunity. Additionally, the non-lytic immunological mechanism, which prevents viral reproduction and reduces cell infection, is included. Caputo fractional derivatives are utilized in each compartment to capture long-term memory effects and non-local behavior. It is demonstrated that the model has nonnegative and bounded solutions. Three equilibrium states are identified in the improved viral model: the virus-clear steady state $\mathcal{G}^{\circ }$, the immunity-free steady state $\mathcal{G}_{1}^{\star}$ and the infection steady state with humoral immunity $\mathcal{G}_{2}^{\star }$. The local stability of the equilibria is investigated using the Routh-Hurwitz criteria and the Matignon condition, while the global stability is shown through the Lyapunov approach and the fractional LaSalle invariance principle. Finally, the theoretical conclusions are validated by numerous numerical simulations.

References

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  • [2] Zhang, S., Li, F. and Xu, X. Dynamics and control strategy for a delayed viral infection model. Journal of Biological Dynamics, 16(1), 44-63, (2022).
  • [3] Kumar, M. and Abbas, S. Global dynamics of an age-structured model for HIV viral dynamics with latently infected T cells. Mathematics and Computers in Simulation, 198, 237-252, (2022).
  • [4] Shaoli, W., Xinlong, F. and Yinnian, H. Global asymptotical properties for a diffused HBV infection model with CTL immune response and nonlinear incidence. Acta Mathematica Scientia, 31(5), 1959-1967, (2011).
  • [5] Wang, X., Elaiw, A. and Song, X. Global properties of a delayed HIV infection model with CTL immune response. Applied Mathematics and Computation, 218(18), 9405-9414, (2012).
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  • [7] AlShamrani, N.H., Alshaikh, M.A., Elaiw, A.M. and Hattaf, K. Dynamics of HIV-1/HTLV-I Co-infection model with humoral immunity and cellular infection. Viruses, 14(8), 1719, (2022).
  • [8] Hattaf, K. and Yousfi, N. Modeling the adaptive immunity and both modes of transmission in HIV infection. Computation, 6(2), 37, (2018).
  • [9] Allali, K., Meskaf, A. and Tridane, A. Mathematical modeling of the adaptive immune responses in the early stage of the HBV infection. International Journal of Differential Equations, 2018, 6710575, (2018).
  • [10] Yang, J. and Wang, L. Dynamics analysis of a delayed HIV infection model with CTL immune response and antibody immune response. Acta Mathematica Scientia, 41(3), 991-1016, (2021).
  • [11] Wodarz, D., Christensen, J. P. and Thomsen, A.R. The importance of lytic and nonlytic immune responses in viral infections. Trends in Immunology, 23(4), 194-200, (2002).
  • [12] Wang, K., Wang, W. and Liu, X. Global stability in a viral infection model with lytic and nonlytic immune responses. Computers & Mathematics with Applications, 51(9-10), 1593-1610, (2006).
  • [13] Vargas-De-Leon, C. Global properties for a virus dynamics model with lytic and non-lytic immune responses, and nonlinear immune attack rates. Journal of Biological Systems, 22(03), 449-462, (2014).
  • [14] Wang, K., Jin, Y. and Fan, A. The effect of immune responses in viral infections: A mathematical model view. Discrete & Continuous Dynamical Systems-Series B, 19(10), 3379-3396, (2014).
  • [15] Dhar, M., Samaddar, S., Bhattacharya, P. and Upadhyay, R.K. Upadhyay. Viral dynamic model with cellular immune response: A case study of HIV-1 infected humanized mice. Physica A: Statistical Mechanics and its Applications, 524, 1-14, (2019).
  • [16] Dhar, M., Samaddar, S. and Bhattacharya, P. Modeling the effect of non-cytolytic immune response on viral infection dynamics in the presence of humoral immunity. Nonlinear Dynamics, 98, 637-655, (2019).
  • [17] Petras, I. Fractional-order nonlinear systems: Modeling, analysis and simulation. Higher Education Press: Beijing, (2011).
  • [18] Baleanu, D., Diethelm, K., Scalas, E. and Trujillo, J.J. Fractional Calculus: Models and Numerical Methods (Vol. 3). World Scientific: Singapore, (2012).
  • [19] Naim, M., Sabbar, Y. and Zeb, A. Stability characterization of a fractional-order viral system with the non-cytolytic immune assumption. Mathematical Modelling and Numerical Simulation with Applications, 2(3), 164-176, (2022).
  • [20] Naim, M., Lahmidi, F. and Namir, A. Global stability of a fractional order SIR epidemic model with double epidemic hypothesis and nonlinear incidence rate. Communications in Mathematical Biology and Neuroscience, 2020, 38, (2020).
  • [21] Gholami, M., Ghaziani, R. K. and Eskandari, Z. Three-dimensional fractional system with the stability condition and chaos control. Mathematical Modelling and Numerical Simulation with Applications, 2(1), 41-47, (2022).
  • [22] Khan, F.M., Khan, Z.U. and Abdullah. Numerical analysis of fractional order drinking mathematical model. Journal of Mathematical Techniques in Modeling, 1(1), 11-24, (2024).
  • [23] Joshi, H., Yavuz, M., Taylan, O. and Alkabaa, A. Dynamic analysis of fractal-fractional cancer model under chemotherapy drug with generalized Mittag-Leffler kernel. Computer Methods and Programs in Biomedicine, 260, 108565, (2025).
  • [24] Iwa, L.L., Omame, A. and Chioma, S. A fractional-order model of COVID-19 and Malaria co-infection. Bulletin of Biomathematics, 2(2), 133-161, (2024).
  • [25] Khan, W.A., Zarin, R., Zeb, A., Khan, Y. and Khan, A. Navigating food allergy dynamics via a novel fractional mathematical model for antacid-induced allergies. Journal of Mathematical Techniques in Modeling, 1(1), 25-51, (2024).
  • [26] Naim, M., Lahmidi, F., Namir, A. and Kouidere, A. Dynamics of a fractional SEIR epidemic model with infectivity in latent period and general nonlinear incidence rate. Chaos, Solitons & Fractals, 152, 111456, (2021).
  • [27] Naim, M., Sabbar, Y., Zahri, M., Ghanbari, B., Zeb, A., Gul, N. et al. The impact of dual time delay and Caputo fractional derivative on the long-run behavior of a viral system with the non-cytolytic immune hypothesis. Physica Scripta, 97(12), 124002, (2022).
  • [28] Sigal, A., Kim, J.T., Balazs, A.B., Dekel, E., Mayo, A., Milo, R. and Baltimore, D. Cell-to-cell spread of HIV permits ongoing replication despite antiretroviral therapy. Nature, 477, 95-98, (2011).
  • [29] Pan, S. and Chakrabarty, S.P. Threshold dynamics of HCV model with cell-to-cell transmission and a non-cytolytic cure in the presence of humoral immunity. Communications in Nonlinear Science and Numerical Simulation, 61, 180-197, (2018).
  • [30] Dhar, M., Samaddar, S. and Bhattacharya, P. Modeling the cell-to-cell transmission dynamics of viral infection under the exposure of non-cytolytic cure. Journal of Applied Mathematics and Computing, 65(1), 885-911, (2021).
  • [31] Naim, M., Yaagoub, Z., Zeb, A., Sadki, M. and Allali, K. Global analysis of a fractional-order viral model with lytic and non-lytic adaptive immunity. Modeling Earth Systems and Environment, 10, 1749-1769, (2023).
  • [32] Hattaf, K., El Karimi, M.I., Mohsen, A.A., Hajhouji, Z., El Younoussi, M. and Yousfi, N. Mathematical modeling and analysis of the dynamics of RNA viruses in presence of immunity and treatment: A case study of SARS-CoV-2. Vaccines, 11(2), 201, (2023).
  • [33] Chen, C. and Zhou, Y. Dynamic analysis of HIV model with a general incidence, CTLs immune response and intracellular delays. Mathematics and Computers in Simulation, 212, 159-181, (2023).
  • [34] Podlubny, I. Fractional Differential Equations. Academic Press: San Diego, (1999).
  • [35] Odibat, Z.M. and Shawagfeh, N.T. Generalized Taylor’s formula. Applied Mathematics and Computation, 186(1), 286-293, (2007).
  • [36] Vargas-De-León, C. Volterra-type Lyapunov functions for fractional-order epidemic systems. Communications in Nonlinear Science and Numerical Simulation, 24(1-3), 75-85, (2015).
  • [37] Aguila-Camacho, N., Duarte-Mermoud, M.A. and Gallegos, J.A. Lyapunov functions for fractional order systems. Communications in Nonlinear Science and Numerical Simulation, 19(9), 2951-2957, (2014).
  • [38] Lin, W. Global existence theory and chaos control of fractional differential equations. Journal of Mathematical Analysis and Applications, 332(1), 709-726, (2007).
  • [39] Li, H.L., Zhang, L., Hu, C., Jiang, Y.L. and Teng, Z. Dynamical analysis of a fractional-order predator-prey model incorporating a prey refuge. Journal of Applied Mathematics and Computing, 54, 435-449, (2017).
  • [40] Kai, D. The Analysis of Fractional Differential Equations. An Application-Oriented Exposition Using Differential Operators of Caputo Type. Springer-Verlag: Berlin, (2010).
  • [41] Ye, M., Li, J. and Jiang, H. Dynamic analysis and optimal control of a novel fractional-order 2I2SR rumor spreading model. Nonlinear Analysis: Modelling and Control, 28(5), 859–882, (2023).
  • [42] Sarmis, M., Orjuela, R., Bouteiller, J.M., Ambert, N., Legendre, A., Bischoff, S. et al. Stability constraints of markov state kinetic models based on routh-hurwitz criterion. Journal of Computer Science and Systems Biology, 8, 296-303, (2015).
  • [43] El-Sayed, A.M.A., Elsonbaty, A., Elsadany, A.A. and Matouk, A.E. Dynamical analysis and circuit simulation of a new fractional-order hyperchaotic system and its discretization. International Journal of Bifurcation and Chaos, 26(13), 1650222, (2016).
  • [44] Huo, J., Zhao, H. and Zhu, L. The effect of vaccines on backward bifurcation in a fractional order HIV model. Nonlinear Analysis: Real World Applications, 26, 289-305, (2015).
  • [45] Naim, M., Lahmidi, F. and Namir, A. Stability analysis of a delayed fractional order SIRS epidemic model with nonlinear incidence rate. International Journal of Applied Mathematics, 32(5), 733-745, (2019).
  • [46] Naim, M., Lahmidi, F. and Namir, A. Mathematical analysis of a fractional order SIS epidemic model with double diseases, Beddington-DeAngelis functional response and time delay. International Journal of Nonlinear Science, 29(1), 47-59, (2020).
  • [47] Yaseen, R.M., Mohsen, A.A., Al-Husseiny, H.F. and Hattaf, K. Stability and Hopf bifurcation of an epidemiological model with effect of delay the awareness programs and vaccination: analysis and simulation. Communications in Mathematical Biology and Neuroscience, 2023, 1-28, (2023).
  • [48] Naim, M. and Lahmidi, F. Analysis of a deterministic and a stochastic SIS epidemic model with double epidemic hypothesis and specific functional response. Dynamics in Nature and Society, 2020(1), 362716, (2020).
  • [49] Din, A., Li, Y. and Yusuf, A. Delayed hepatitis B epidemic model with stochastic analysis. Chaos, Solitons & Fractals, 146, 110839, (2021).
Year 2024, Volume: 4 Issue: 5-Special Issue: ICAME'24, 94 - 115, 31.12.2024
https://doi.org/10.53391/mmnsa.1517325

Abstract

References

  • [1] Li, C., Dong, X. and Wang, J. Stability analysis of an age-structured viral infection model with latency. Electronic Journal of Differential Equations, 2022(16), 1–26, (2022).
  • [2] Zhang, S., Li, F. and Xu, X. Dynamics and control strategy for a delayed viral infection model. Journal of Biological Dynamics, 16(1), 44-63, (2022).
  • [3] Kumar, M. and Abbas, S. Global dynamics of an age-structured model for HIV viral dynamics with latently infected T cells. Mathematics and Computers in Simulation, 198, 237-252, (2022).
  • [4] Shaoli, W., Xinlong, F. and Yinnian, H. Global asymptotical properties for a diffused HBV infection model with CTL immune response and nonlinear incidence. Acta Mathematica Scientia, 31(5), 1959-1967, (2011).
  • [5] Wang, X., Elaiw, A. and Song, X. Global properties of a delayed HIV infection model with CTL immune response. Applied Mathematics and Computation, 218(18), 9405-9414, (2012).
  • [6] Wang, S. and Zou, D. Global stability of in-host viral models with humoral immunity and intracellular delays. Applied Mathematical Modelling, 36(3), 1313-1322, (2012).
  • [7] AlShamrani, N.H., Alshaikh, M.A., Elaiw, A.M. and Hattaf, K. Dynamics of HIV-1/HTLV-I Co-infection model with humoral immunity and cellular infection. Viruses, 14(8), 1719, (2022).
  • [8] Hattaf, K. and Yousfi, N. Modeling the adaptive immunity and both modes of transmission in HIV infection. Computation, 6(2), 37, (2018).
  • [9] Allali, K., Meskaf, A. and Tridane, A. Mathematical modeling of the adaptive immune responses in the early stage of the HBV infection. International Journal of Differential Equations, 2018, 6710575, (2018).
  • [10] Yang, J. and Wang, L. Dynamics analysis of a delayed HIV infection model with CTL immune response and antibody immune response. Acta Mathematica Scientia, 41(3), 991-1016, (2021).
  • [11] Wodarz, D., Christensen, J. P. and Thomsen, A.R. The importance of lytic and nonlytic immune responses in viral infections. Trends in Immunology, 23(4), 194-200, (2002).
  • [12] Wang, K., Wang, W. and Liu, X. Global stability in a viral infection model with lytic and nonlytic immune responses. Computers & Mathematics with Applications, 51(9-10), 1593-1610, (2006).
  • [13] Vargas-De-Leon, C. Global properties for a virus dynamics model with lytic and non-lytic immune responses, and nonlinear immune attack rates. Journal of Biological Systems, 22(03), 449-462, (2014).
  • [14] Wang, K., Jin, Y. and Fan, A. The effect of immune responses in viral infections: A mathematical model view. Discrete & Continuous Dynamical Systems-Series B, 19(10), 3379-3396, (2014).
  • [15] Dhar, M., Samaddar, S., Bhattacharya, P. and Upadhyay, R.K. Upadhyay. Viral dynamic model with cellular immune response: A case study of HIV-1 infected humanized mice. Physica A: Statistical Mechanics and its Applications, 524, 1-14, (2019).
  • [16] Dhar, M., Samaddar, S. and Bhattacharya, P. Modeling the effect of non-cytolytic immune response on viral infection dynamics in the presence of humoral immunity. Nonlinear Dynamics, 98, 637-655, (2019).
  • [17] Petras, I. Fractional-order nonlinear systems: Modeling, analysis and simulation. Higher Education Press: Beijing, (2011).
  • [18] Baleanu, D., Diethelm, K., Scalas, E. and Trujillo, J.J. Fractional Calculus: Models and Numerical Methods (Vol. 3). World Scientific: Singapore, (2012).
  • [19] Naim, M., Sabbar, Y. and Zeb, A. Stability characterization of a fractional-order viral system with the non-cytolytic immune assumption. Mathematical Modelling and Numerical Simulation with Applications, 2(3), 164-176, (2022).
  • [20] Naim, M., Lahmidi, F. and Namir, A. Global stability of a fractional order SIR epidemic model with double epidemic hypothesis and nonlinear incidence rate. Communications in Mathematical Biology and Neuroscience, 2020, 38, (2020).
  • [21] Gholami, M., Ghaziani, R. K. and Eskandari, Z. Three-dimensional fractional system with the stability condition and chaos control. Mathematical Modelling and Numerical Simulation with Applications, 2(1), 41-47, (2022).
  • [22] Khan, F.M., Khan, Z.U. and Abdullah. Numerical analysis of fractional order drinking mathematical model. Journal of Mathematical Techniques in Modeling, 1(1), 11-24, (2024).
  • [23] Joshi, H., Yavuz, M., Taylan, O. and Alkabaa, A. Dynamic analysis of fractal-fractional cancer model under chemotherapy drug with generalized Mittag-Leffler kernel. Computer Methods and Programs in Biomedicine, 260, 108565, (2025).
  • [24] Iwa, L.L., Omame, A. and Chioma, S. A fractional-order model of COVID-19 and Malaria co-infection. Bulletin of Biomathematics, 2(2), 133-161, (2024).
  • [25] Khan, W.A., Zarin, R., Zeb, A., Khan, Y. and Khan, A. Navigating food allergy dynamics via a novel fractional mathematical model for antacid-induced allergies. Journal of Mathematical Techniques in Modeling, 1(1), 25-51, (2024).
  • [26] Naim, M., Lahmidi, F., Namir, A. and Kouidere, A. Dynamics of a fractional SEIR epidemic model with infectivity in latent period and general nonlinear incidence rate. Chaos, Solitons & Fractals, 152, 111456, (2021).
  • [27] Naim, M., Sabbar, Y., Zahri, M., Ghanbari, B., Zeb, A., Gul, N. et al. The impact of dual time delay and Caputo fractional derivative on the long-run behavior of a viral system with the non-cytolytic immune hypothesis. Physica Scripta, 97(12), 124002, (2022).
  • [28] Sigal, A., Kim, J.T., Balazs, A.B., Dekel, E., Mayo, A., Milo, R. and Baltimore, D. Cell-to-cell spread of HIV permits ongoing replication despite antiretroviral therapy. Nature, 477, 95-98, (2011).
  • [29] Pan, S. and Chakrabarty, S.P. Threshold dynamics of HCV model with cell-to-cell transmission and a non-cytolytic cure in the presence of humoral immunity. Communications in Nonlinear Science and Numerical Simulation, 61, 180-197, (2018).
  • [30] Dhar, M., Samaddar, S. and Bhattacharya, P. Modeling the cell-to-cell transmission dynamics of viral infection under the exposure of non-cytolytic cure. Journal of Applied Mathematics and Computing, 65(1), 885-911, (2021).
  • [31] Naim, M., Yaagoub, Z., Zeb, A., Sadki, M. and Allali, K. Global analysis of a fractional-order viral model with lytic and non-lytic adaptive immunity. Modeling Earth Systems and Environment, 10, 1749-1769, (2023).
  • [32] Hattaf, K., El Karimi, M.I., Mohsen, A.A., Hajhouji, Z., El Younoussi, M. and Yousfi, N. Mathematical modeling and analysis of the dynamics of RNA viruses in presence of immunity and treatment: A case study of SARS-CoV-2. Vaccines, 11(2), 201, (2023).
  • [33] Chen, C. and Zhou, Y. Dynamic analysis of HIV model with a general incidence, CTLs immune response and intracellular delays. Mathematics and Computers in Simulation, 212, 159-181, (2023).
  • [34] Podlubny, I. Fractional Differential Equations. Academic Press: San Diego, (1999).
  • [35] Odibat, Z.M. and Shawagfeh, N.T. Generalized Taylor’s formula. Applied Mathematics and Computation, 186(1), 286-293, (2007).
  • [36] Vargas-De-León, C. Volterra-type Lyapunov functions for fractional-order epidemic systems. Communications in Nonlinear Science and Numerical Simulation, 24(1-3), 75-85, (2015).
  • [37] Aguila-Camacho, N., Duarte-Mermoud, M.A. and Gallegos, J.A. Lyapunov functions for fractional order systems. Communications in Nonlinear Science and Numerical Simulation, 19(9), 2951-2957, (2014).
  • [38] Lin, W. Global existence theory and chaos control of fractional differential equations. Journal of Mathematical Analysis and Applications, 332(1), 709-726, (2007).
  • [39] Li, H.L., Zhang, L., Hu, C., Jiang, Y.L. and Teng, Z. Dynamical analysis of a fractional-order predator-prey model incorporating a prey refuge. Journal of Applied Mathematics and Computing, 54, 435-449, (2017).
  • [40] Kai, D. The Analysis of Fractional Differential Equations. An Application-Oriented Exposition Using Differential Operators of Caputo Type. Springer-Verlag: Berlin, (2010).
  • [41] Ye, M., Li, J. and Jiang, H. Dynamic analysis and optimal control of a novel fractional-order 2I2SR rumor spreading model. Nonlinear Analysis: Modelling and Control, 28(5), 859–882, (2023).
  • [42] Sarmis, M., Orjuela, R., Bouteiller, J.M., Ambert, N., Legendre, A., Bischoff, S. et al. Stability constraints of markov state kinetic models based on routh-hurwitz criterion. Journal of Computer Science and Systems Biology, 8, 296-303, (2015).
  • [43] El-Sayed, A.M.A., Elsonbaty, A., Elsadany, A.A. and Matouk, A.E. Dynamical analysis and circuit simulation of a new fractional-order hyperchaotic system and its discretization. International Journal of Bifurcation and Chaos, 26(13), 1650222, (2016).
  • [44] Huo, J., Zhao, H. and Zhu, L. The effect of vaccines on backward bifurcation in a fractional order HIV model. Nonlinear Analysis: Real World Applications, 26, 289-305, (2015).
  • [45] Naim, M., Lahmidi, F. and Namir, A. Stability analysis of a delayed fractional order SIRS epidemic model with nonlinear incidence rate. International Journal of Applied Mathematics, 32(5), 733-745, (2019).
  • [46] Naim, M., Lahmidi, F. and Namir, A. Mathematical analysis of a fractional order SIS epidemic model with double diseases, Beddington-DeAngelis functional response and time delay. International Journal of Nonlinear Science, 29(1), 47-59, (2020).
  • [47] Yaseen, R.M., Mohsen, A.A., Al-Husseiny, H.F. and Hattaf, K. Stability and Hopf bifurcation of an epidemiological model with effect of delay the awareness programs and vaccination: analysis and simulation. Communications in Mathematical Biology and Neuroscience, 2023, 1-28, (2023).
  • [48] Naim, M. and Lahmidi, F. Analysis of a deterministic and a stochastic SIS epidemic model with double epidemic hypothesis and specific functional response. Dynamics in Nature and Society, 2020(1), 362716, (2020).
  • [49] Din, A., Li, Y. and Yusuf, A. Delayed hepatitis B epidemic model with stochastic analysis. Chaos, Solitons & Fractals, 146, 110839, (2021).
There are 49 citations in total.

Details

Primary Language English
Subjects Biological Mathematics
Journal Section Research Articles
Authors

Mouhcine Naim 0000-0002-6130-3633

Anwar Zeb 0000-0002-5460-3718

Ahmed Ali Mohsen 0000-0003-3812-8918

Yassine Sabbar 0000-0002-1127-4395

Mustafa Yıldız 0000-0003-3367-7176

Publication Date December 31, 2024
Submission Date July 16, 2024
Acceptance Date December 27, 2024
Published in Issue Year 2024 Volume: 4 Issue: 5-Special Issue: ICAME'24

Cite

APA Naim, M., Zeb, A., Mohsen, A. A., Sabbar, Y., et al. (2024). Local and global stability of a fractional viral infection model with two routes of propagation, cure rate and non-lytic humoral immunity. Mathematical Modelling and Numerical Simulation With Applications, 4(5-Special Issue: ICAME’24), 94-115. https://doi.org/10.53391/mmnsa.1517325


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