ArticleJournal of acquired immune deficiency syndromes (1999)2019
Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data.
Article in Journal of acquired immune deficiency syndromes (1999), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Social network isolation and cohesion and their association with HIV clustering among Black and Latino sexual minority men: implications for public health strategy.Scientific reports · 2026Article
- Sequencing HIV Diagnostic Samples to Detect Genetic Clusters and Assess Sequence Coverage Gaps.Open forum infectious diseases · 2025Article
- Analysis of HIV transmission characteristics and intervention effects in Guangxi based on molecular networks.AIDS (London, England) · 2025Article
- Dynamics of clustering rates in the Rhode Island HIV-1 epidemic.AIDS (London, England) · 2025Article
- DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.PLoS computational biology · 2024Article
- Modeling homophily in dynamic networks with application to HIV molecular surveillance.BMC infectious diseases · 2023Article
- Using molecular network analysis to understand current HIV-1 transmission characteristics in an inland area of Yunnan, China.Epidemiology and infection · 2023Article
- Predictors of HIV Molecular Cluster Membership and Implications for Partner Services.AIDS research and human retroviruses · 2023Article
- From components to communities: bringing network science to clustering for molecular epidemiology.Virus evolution · 2023Article
- Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.PLoS computational biology · 2022Article
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- Intersection of Syphilis and Human Immunodeficiency Virus (HIV) Networks to Identify Opportunities to Enhance HIV Prevention.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Using molecular network analysis to explore the characteristics of HIV-1 transmission in a China-Myanmar border area.PloS one · 2022Article
- Longitudinal typing of molecular HIV clusters in a statewide epidemic.AIDS (London, England) · 2021Article
- Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.PLoS computational biology · 2021Article
- HIV-1 Transmission linkages among persons with incident infection to inform public health surveillance.EClinicalMedicine · 2021Article
- Near Real-Time Identification of Recent Human Immunodeficiency Virus Transmissions, Transmitted Drug Resistance Mutations, and Transmission Networks by Multiplexed Primer ID-Next-Generation Sequencing in North Carolina.The Journal of infectious diseases · 2021Article
- HIV Outbreak Control With Effective Access to Care and Harm Reduction in North Carolina, 2017-2018.American journal of public health · 2020Article
- Factors Associated With Human Immunodeficiency Virus Infections Linked in Genetic Clusters But Disconnected in Partner Tracing.Sexually transmitted diseases · 2020Article
- Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundPrediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data.
methodsWe identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017.
resultsCluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples.
conclusionsThe predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.
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