A review of network models for HIV spread

Background: HIV/AIDS has been a global health crisis for over four decades. Network models, which simulate human behavior and intervention impacts, have become an essential tool in guiding HIV prevention strategies and policies. However, no comprehensive survey of network models in HIV research has been conducted. This paper fills that gap, offering a summary of past work and future directions to engage more researchers and inform policy related to eliminating HIV.

Setting: Network models explicitly represent interactions between individuals, making them well-suited to study HIV transmission dynamics. Two primary modeling paradigms exist: a mechanistic approach from applied mathematics and a statistical approach from the social sciences. Each has distinct strengths and weaknesses, which should be understood for effective application to HIV research.

Methods: We conducted a systematic review of network models used in HIV research, detailing the model types, populations, interventions, behaviors, datasets, and software employed, while identifying potential future research directions.

Results: Network models are particularly valuable for studying behaviors central to HIV transmission, such as partner selection and treatment adherence. Unlike traditional models, they focus on indi- vidual behaviors, aligning them with clinical practice. However, more accurate network data are needed for better model calibration and actionable insights.

Conclusion: This paper serves as a point of reference for HIV researchers interested in applying network models and understanding their limitations. To our knowledge, this is the most comprehensive review of HIV network models to date.