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Effects of diffusion and delayed immune response on dynamic behavior in a viral model

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  • Alfifi, H.Y.

Abstract

This paper studies a diffusive viral infection system with delayed immune response in the one-domain system. A system of DDE equations was explored, both analytically and numerically, using the Galerkin method. A condition that helps to find Hopf bifurcation points is determined. Full maps of the Hopf bifurcation points as well regions of stability are constructed and considered in detail. It is shown that the time delay of cytotoxic T lymphocyte (CTL) response and the diffusion parameter can significantly impact upon the stability regions. Furthermore, the influences of the other free values have been examined for their effects on stability. It is found that, as diffusion increases, the CTL response delay increases, and also as the CTL response delay is increased, the Hopf points for both generation rate and activate rate are decreased, whereas the Hopf points for the infection and death rates increased. Moreover, an increase diffusion results in an increase in the Hopf points for growth rate and activation rate, while the Hopf bifurcations are decreased for the death rate of infected cells. Bifurcation diagrams are plotted to show selected examples of limit cycle behavior (periodic oscillation), and 3-D solutions for the three concentrations in the model have been plotted to corroborate all analytical results from the theoretical section.

Suggested Citation

  • Alfifi, H.Y., 2023. "Effects of diffusion and delayed immune response on dynamic behavior in a viral model," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    • Handle: RePEc:eee:apmaco:v:441:y:2023:i:c:s0096300322007822
    DOI: 10.1016/j.amc.2022.127714
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    References listed on IDEAS

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    1. Gharahasanlou, Tohid Kasbi & Roomi, Vahid & Hemmatzadeh, Zeynab, 2022. "Global stability analysis of viral infection model with logistic growth rate, general incidence function and cellular immunity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 64-79.
    2. H. Y. Alfifi & Baogui Xin, 2021. "Semi-Analytical Solutions for the Diffusive Kaldor–Kalecki Business Cycle Model with a Time Delay for Gross Product and Capital Stock," Complexity, Hindawi, vol. 2021, pages 1-10, May.
    3. Alfifi, H.Y., 2022. "Stability analysis for Schnakenberg reaction-diffusion model with gene expression time delay," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Canabarro, A.A. & Gléria, I.M. & Lyra, M.L., 2004. "Periodic solutions and chaos in a non-linear model for the delayed cellular immune response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(1), pages 234-241.
    5. Bera, Sovan & Khajanchi, Subhas & Roy, Tapan Kumar, 2022. "Dynamics of an HTLV-I infection model with delayed CTLs immune response," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    6. Alfifi, H.Y., 2021. "Stability and Hopf bifurcation analysis for the diffusive delay logistic population model with spatially heterogeneous environment," Applied Mathematics and Computation, Elsevier, vol. 408(C).
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