Network typologies predict future molecular linkages in the network of HIV transmission.
Network typologies predict future molecular linkages in the network of HIV transmission.
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DOI：
10.1097/qad.0000000000003621
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发表时间：
2023-09-01
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影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
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HIV分子传播网络类型先前已证明与传播风险相关；然而，很少有研究评估它们在预测未来传播事件方面的预测潜力。为了评估这一点，我们根据佛罗里达州卫生部的全州监测数据测试了多个模型。这是一项回顾性、观察性队列研究，研究了佛罗里达州艾滋病毒感染者 (PWH) 现有分子网络中新的艾滋病毒分子联系的发生率。使用 HIV-TRAnsmission Cluster Engine (HIV-TRACE) 为 2006 年至 2017 年在佛罗里达州诊断的 PWH 重建了 HIV-1 分子传播簇。使用各种人口统计、临床和网络衍生参数对一套旨在预测与新诊断的联系的机器学习模型进行了内部和暂时的外部验证。 2012 年至 2017 年期间，在诊断后 12 个月内获得基因型的 9897 人中，2611 人 (26.4%) 与 1 年内的另一病例存在分子关联，遗传距离为 1.5%。表现最好的模型经过两年的数据训练，表现良好（接收操作曲线下面积 = 0.96，灵敏度 = 0.91，特异性 = 0.90），并包括以下变量：年龄组、暴露组、节点度、介数、传递性和邻域。在佛罗里达州艾滋病毒传播的分子网络中，个体的网络位置和连接性预测了未来的分子联系。使用网络类型的机器学习模型的性能优于仅使用单个数据的模型。这些模型可用于更精确地识别需要干预的亚群。
HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health. This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida. HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters. Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012–2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood. In the molecular network of HIV transmission in Florida, individuals’ network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.
影响因子：
3.8
作者：
Singleton AL;Marshall BDL;Bessey S;Harrison MT;Galvani AP;Yedinak JL;Jacka BP;Goodreau SM;Goedel WC
通讯作者：
Goedel WC