From giant components to communities: community-level insights for prioritizing interventions within large clusters of HIV-1 transmission networks

Large clusters in HIV-1 molecular network 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 ( P < 0.001) and phylogenetic analysis identified Community 1 as the most likely ancestral source of the giant component (marginal probability = 0.998). 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.