Inference for Within- and Between-Partnership Transmission Rates for HIV Infection

2026-02-04

AI Summary

Accurately estimating the rates of HIV transmission within and outside serodiscordant couples is a critical challenge for designing effective prevention strategies. This study proposes a stochastic susceptible–infected (SI) couple model that integrates likelihood-based parameter inference with uncertainty quantification, incorporating— for the first time at the couple level—sex-specific differences in infection dynamics. The framework successfully estimates both internal and external transmission rates along with their confidence intervals, providing a quantitative foundation for targeted interventions. Furthermore, the approach demonstrates generalizability and can be adapted to similar modeling scenarios for other infectious diseases.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

HIV transmission within serodiscordant couples remains a significant public health challenge, particularly in sub-Saharan Africa. Estimating the rate of such infection, alongside the rates of introduction of infection from outside the partnership, is a special case of the more general epidemiological challenge of inferring intensities of within- and between-group intensities of transmission. This study presents a stochastic susceptible-infected (SI) pair model for estimating key epidemiological parameters governing HIV transmission within and between couples, which we further extend to account for gender-specific differences in infection dynamics. Using a likelihood-based inference approach, we estimate transmission parameters and associated uncertainty from observed data. These values can be used to inform infection prevention strategies for HIV, and the methodology proposed can be generalised to other epidemiological settings.

Problem

• HIV transmission
• serodiscordant couples
• within-partnership transmission
• between-partnership transmission
• epidemiological inference

Innovation

• stochastic SI pair model
• within- and between-partnership transmission
• likelihood-based inference
• gender-specific transmission dynamics
• HIV epidemiology