Generic Patterns in HIV Transmission Dynamics: Insights from a Phenomenological Risk-Stratified Modeling Approach

Background: Understanding the dynamics of HIV transmission in heterogeneous populations is crucial for effective prevention strategies. This study introduces the Risk Modulation Point (RMP), a novel threshold identifying where HIV transmission transitions from unsustainable spread to self-sustaining epidemic dynamics. Methods: Using a deterministic, risk-stratified compartmental model, we examined HIV transmission across populations stratified into 100–200 risk groups, each characterized by behavioral heterogeneity modeled through a power-law distribution. The model captures key features of HIV progression, with simulations conducted across high- (~20%), moderate- (~5%), and low (~0.2%)-prevalence regimes. Results: Our findings reveal universal patterns in HIV dynamics. The RMP marks a consistent threshold across scenarios, separating low-risk groups where transmission is minimal from higher-risk groups sustaining the epidemic. Logistic growth in HIV prevalence across risk groups, with sharp transitions near the RMP, was observed universally. The force of infection follows power-law scaling, directly reflecting the level and nature of risk behavior within each group. Importantly, the location of the RMP remains largely invariant to the underlying sexual risk distribution, population resolution, and mixing patterns, making it applicable across both generalized and concentrated epidemics. Conclusion: The RMP framework offers actionable public health insights. It identifies key populations and transition regions for targeted interventions such as antiretroviral therapy and pre-exposure prophylaxis. By tracking shifts in the RMP, it also serves as an early warning indicator for epidemic transitions, guiding resource allocation and monitoring. The focus of the model on intrinsic epidemic dynamics, excluding external interventions, highlights its utility in uncovering fundamental transmission patterns. This study bridges theoretical modeling and practical application, providing a flexible framework for understanding HIV and other stratified epidemics. The findings advance HIV modeling by revealing generic patterns that transcend specific contexts, supporting data-driven public health strategies.