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

There are multiple modes for human immunodeficiency virus (HIV) transmissions, each of which is usually associated with a certain key population (e.g., needle sharing among people who inject drugs). Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this paper, we aim to address this challenge by developing a multiplex social network framework to capture the multimode transmission across two key populations. Based on the multiplex social network framework, we propose a new random search method, named partition-based random search with network and memory prioritization (PRS-NMP), to identify the optimal subset of high-value individuals in the social network for interventions. Numerical experiments demonstrated that the proposed PRS-NMP-based interventions could effectively reduce the scale of HIV transmissions. The performance of PRS-NMP-based interventions is consistently better than the benchmark nested partitions method and network-based metrics.