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

There are multiple modes for 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 research, we aim to address this challenge by developing a multiplex social network framework to capture the multi-mode 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 benchmark Nested Partitions method and network-based metrics.