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Mathematical modelling and simulation of HIV dynamics
arXiv:2512.13171v1 Announce Type: new
Abstract: We study within-host HIV dynamics using a three--component nonlinear ordinary differential equation model for healthy CD4$^{+}$ T cells, infected CD4$^{+}$ T cells, and free virus. In addition to the baseline model without treatment, we consider two treatment extensions that incorporate antiretroviral therapy: (i) separate efficacy terms for Reverse Transcriptase inhibitors and Protease inhibitors acting on infection and virus production, and (ii) a simplified formulation using a single combined efficacy parameter. For each model we determine equilibrium points and apply linearization to obtain the Jacobian matrix and local stability conditions near equilibrium. We perform numerical simulations using the classical fourth--order Runge--Kutta method to illustrate the evolution of cell populations and viral load under different therapy levels and treatment start times, including continuous treatment scenarios. The simulations highlight how therapy strength and timing shape transient behavior and can lead to sustained viral suppression.
Abstract: We study within-host HIV dynamics using a three--component nonlinear ordinary differential equation model for healthy CD4$^{+}$ T cells, infected CD4$^{+}$ T cells, and free virus. In addition to the baseline model without treatment, we consider two treatment extensions that incorporate antiretroviral therapy: (i) separate efficacy terms for Reverse Transcriptase inhibitors and Protease inhibitors acting on infection and virus production, and (ii) a simplified formulation using a single combined efficacy parameter. For each model we determine equilibrium points and apply linearization to obtain the Jacobian matrix and local stability conditions near equilibrium. We perform numerical simulations using the classical fourth--order Runge--Kutta method to illustrate the evolution of cell populations and viral load under different therapy levels and treatment start times, including continuous treatment scenarios. The simulations highlight how therapy strength and timing shape transient behavior and can lead to sustained viral suppression.
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