网络类型学预测了艾滋病毒传播网络中的未来分子联系
Shannan N Rich1,2, Robert L Cook1,2, Carla N Mavian2,3
1Department of Epidemiology, Colleges of Public Health and Health Professions and Medicine.
AIDS (London, England)
|June 8, 2023
概括
艾滋病毒传播网络中的网络位置和连接性可以预测未来的联系. 使用这些网络特征的机器学习模型在识别风险人群时优于个人数据模型.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 艾滋病毒分子传播网络类型学显示了与传播风险的关联.
- 很少有研究评估了这些类型学对未来传播事件的预测潜力.
研究的目的:
- 评估HIV分子传播网络类型学的预测潜力,以预测未来的传播事件.
- 在全州监测数据上测试多个机器学习模型,以预测新的HIV分子联系.
主要方法:
- 使用佛罗里达州卫生部数据 (2006-2017年) 进行的回顾性观察队列研究.
- 使用HIV-TRAnsmission集群引擎 (HIV-TRACE) 重建的HIV-1分子传播集群.
- 通过使用人口,临床和网络衍生参数验证的机器学习模型.
主要成果:
- 在9897个个体中,26.4%的个体在一年内与另一个病例有分子联系.
- 性能最好的模型实现了高精度 (AUC=0.96,灵敏度=0.91,特异性=0.90).
- 关键的预测变量包括年龄组,暴露组,节点程度,中间,过渡性和邻近.
结论:
- 个体的网络位置和连接性预测了艾滋病毒传播网络中未来的分子联系.
- 结合网络类型的机器学习模型仅仅使用个人数据的模型的性能优于网络类型的机器学习模型.
- 这些预测模型可以识别针对性艾滋病毒干预的子群体.
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