从巨大连通分量到社区：社区层面的洞见用于在HIV-1传播网络大型聚集群中优先确定干预措施

摘要(中文译文)

※ 中文译文由 AI 辅助生成,仅供学术参考,请以英文原文为准。

HIV-1分子网络中的大簇包含相当比例的人类免疫缺陷病毒感染者，并主导着局部疫情；然而，其庞大的规模和复杂的结构对有效的公共卫生干预构成了挑战。我们开发了一个分析框架，将大簇划分为若干小群组以进行精准干预。在中国广州的HIV-1 CRF07_BC分子传播网络（2008—2020年）中，一个巨型组分（681名成员）被划分为34个内部连接密集、外部连接稀疏的社群。所有378条社群间连接均涉及来自社群1的高中心性成员（< 0.001），系统发育分析确定社群1为该巨型组分最可能的祖源来源（边际概率 = 0.989）。指数随机图模型（ERGMs）揭示了巨型组分及其大型社群中具有特定特征成员之间显著的同类相聚效应，凸显了特定亚群内潜在的疫情暴发风险。将巨型组分划分为社群，可能是帮助制定社群层面干预措施的一种有前景的方法，并有望提高针对大簇的干预措施的有效性。

Abstract

Large clusters in HIV-1 molecular networks contain a substantial proportion of people living with HIV and dominate local epidemics; however, their large size and complex structure pose a challenge for effective public health interventions. We developed an analytical framework to partition the large cluster into small groups for precise intervention. In the HIV-1 CRF07_BC molecular transmission network in Guangzhou, China (2008-2020), a giant component (681 members) was partitioned into 34 communities with dense internal and sparse external links. All 378 inter-community links involved high-centrality members from Community 1 ( < 0.001) and phylogenetic analysis identified Community 1 as the most likely ancestral source of the giant component (marginal probability = 0.989). Exponential random graph models (ERGMs) revealed significant homophily effect among members with specific characteristics in the giant component and its large communities, highlighting potential outbreaks within specific subgroups. Partitioning giant components into communities may be a promising approach to help develop community-level interventions and could improve the effectiveness of interventions targeted at large clusters.

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