Optimizing HIV Interventions for Multiplex Social Networks via Partition-Based Random Search

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Human immunodeficiency virus (HIV) transmission occurs through various modes, often linked to specific key populations.
• Merging trends in key populations create complex social networks, complicating HIV interventions.
• Existing intervention strategies struggle with the challenge of multimode transmission in multiplex networks.

Purpose of the Study:

• To develop a multiplex social network framework for capturing multimode HIV transmission.
• To propose an optimized intervention strategy for complex social networks.
• To identify high-value individuals within social networks for effective HIV prevention.

Main Methods:

• Development of a multiplex social network framework to model HIV transmission across two key populations.
• Proposal of a novel partition-based random search with network and memory prioritization (PRS-NMP) algorithm.
• Numerical experiments to evaluate the efficacy of PRS-NMP-based interventions.

Main Results:

• The proposed PRS-NMP method effectively identifies optimal subsets of individuals for HIV interventions.
• Interventions based on PRS-NMP significantly reduced the scale of HIV transmissions in simulations.
• PRS-NMP demonstrated superior performance compared to benchmark methods like nested partitions and network-based metrics.

Conclusions:

• The developed multiplex social network framework provides a robust approach to understanding complex HIV transmission dynamics.
• The PRS-NMP algorithm offers an effective strategy for targeted HIV interventions in merged populations.
• This approach holds promise for improving public health outcomes in HIV prevention efforts.