University of Massachusetts Amherst, Amherst, MA, United States.
Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Math Biosci Eng. 2021 Mar 3;18(3):2150-2181. doi: 10.3934/mbe.2021109.
We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015-2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan.
我们展示了 HIV 进展和传播(PATH 4.0),这是一种用于分析集群检测和干预策略的模拟工具。分子集群是指遗传上相似的 HIV 感染群体,表明 HIV 预防资源需要得到改善,以提高健康结果并预防新的感染。PATH 4.0 是使用新开发的基于代理的进化网络建模 (ABENM) 技术和进化接触网络算法 (ECNA) 构建的,用于生成无标度网络。ABENM 和 ECNA 是为了促进 HIV 等低流行疾病的传播网络模拟而开发的,这为当前的网络模拟技术带来了计算挑战。模拟传播网络对于研究网络动态(包括集群)至关重要。我们通过将 PATH 4.0 模拟的 HIV 诊断预测与国家 HIV 监测系统(NHSS)2010-2017 年的估计进行比较来验证 PATH 4.0。我们还将集群生成算法应用于 PATH 4.0,以估计集群特征,包括按集群状态和大小以及集群大小分布划分的诊断为 HIV 感染者的分布。模拟特征与 NHSS 估计值非常吻合,NHSS 估计值使用分子方法来检测 2015-2017 年期间诊断为 HIV 的人的 HIV 核苷酸序列中的集群。集群检测和响应是美国终结 HIV 流行策略的一个组成部分。虽然监测对于检测集群至关重要,但模型与监测相结合可以使我们改进集群检测方法,了解与集群增长相关的因素,并评估干预措施,以制定有效的应对策略。由于监测数据仅可用于诊断和报告的病例,因此模型是了解集群真实规模并评估关键问题(例如集群对后续传播的相对贡献)的关键工具。我们相信 PATH 4.0 是第一个可用于评估国家层面集群检测和响应的建模工具,并可以帮助为国家战略计划提供信息。