Transmission dynamics and stability analysis of fractional HIV and AIDS epidemic model with antiretroviral therapy

2025

Scientific Reports, Vol 15, Iss 1, Pp 1-33 (2025)

Abstract Worldwide populations have historically experienced serious issues from infectious diseases, requiring coordinated and inclusive prevention measures. HIV is one of the most hazardous of these as it attacks CD4 + cells, or T-cell lymphocytes, which are crucial to human immunity. To explore the variability of HIV/AIDS transmission, this study introduces a nonlinear stochastic mathematical model that incorporates a recovery compartment to account for hospitalized patients’ progression to complete recovery and to better capture the intricate dynamics of disease transmission. Fractional derivatives are used with a generalized Caputo operator to enhance the accuracy of the model, effectively mixing the memory and genes that exist in biological systems. The model’s validity is affirmed through considerations of positivity, boundedness, reproduction number, stability, and sensitivity analysis. Stability theory is employed to explore both local and global stabilities. Sensitivity analysis identifies parameters with a significant impact on the reproduction number. To establish the existence and uniqueness of solutions, the model is qualitatively examined via fixed-point theory. Apart from that, a new numerical technique for simulations focused on the predictor-corrector strategy is implemented and MATLAB is used to verify the results. By comparing the fractional-order and integer-order derivatives, it is noted that the fractional-order method is the more accurate and realistic depiction of the dynamics of the disease. The suggested technique unlocks the door for more effective interventions giving researchers a competitive edge in learning about and managing the complex mechanisms of HIV/AIDS transmission.

article

English

Nature Portfolio, 2025.

HIV-AIDS epidemic model, Existence and uniqueness, Generalized Caputo derivative, Adaptive P–C approach, Numerical simulation, Medicine, Science