臨床感染症と実践

抽象的な

Predicting Disease Progression: A Stochastic Model of HIV with Latent Infection and Antiretroviral Therapy

Nahed Seddiq

Mathematical models play a crucial role in understanding the dynamics of HIV infection and evaluating the impact of interventions such as antiretroviral therapy (ART). This article presents a stochastic HIV infection model that incorporates latent infection and the effects of ART. The model accounts for the inherent variability and randomness observed in HIV infection dynamics, providing valuable insights into disease progression, treatment outcomes, and control strategies. The inclusion of a latent infection stage captures the persistence of the virus and its potential for reactivation. Additionally, the model considers the impact of ART on viral load reduction, immune restoration, and the prevention of disease progression. By incorporating stochastic elements, the model reflects the biological variability and uncertainties associated with HIV infection, aiding in predicting long-term outcomes and informing decision-making processes. Continued research and refinement of such models contribute to our understanding of HIV pathogenesis and the development of more effective interventions to combat the global HIV/AIDS epidemic.