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.

中文翻译：

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通过基于分区的随机搜索优化多重社交网络的艾滋病毒干预措施


人类免疫缺陷病毒（HIV）传播有多种模式，每种模式通常都与某个关键人群相关（例如注射吸毒者共用针头）。最近的实地研究揭示了多个重点人群的融合趋势，由于这种复杂的多重社交网络中存在多种传播模式，使得艾滋病毒干预变得困难。在本文中，我们的目标是通过开发一个多重社交网络框架来捕获跨两个关键人群的多模式传输来应对这一挑战。基于多重社交网络框架，我们提出了一种新的随机搜索方法，称为基于分区的随机搜索与网络和内存优先级（PRS-NMP），以识别社交网络中高价值个体的最佳子集进行干预。数值实验表明，所提出的基于 PRS-NMP 的干预措施可以有效减少 HIV 传播的规模。基于 PRS-NMP 的干预措施的性能始终优于基准嵌套分区方法和基于网络的指标。