Next-Generation Phylodynamics-targeted Partner Service Models for Combined HIV Prevention

以系统动力学为目标的合作伙伴服务模型（P2 M）项目旨在指导和快速转变 不断发展的公共卫生实施分子艾滋病毒监测（MHS）为基础的预防干预措施， 这是消除艾滋病毒的关键一步。P2 M旨在研究和优化新兴的国家指导： CDC的2017年3月的建议，艾滋病毒集群指导工作组对卫生部门 实施具有临床HIV耐药基因型的MHS，现在建议在进入护理时进行。这些HIV-1 pol序列可用于鉴定具有遗传相似HIV的人的“分子簇”。的 MHS的首要目标是识别风险环境中的人员。这些人是潜在的线人 新的HIV传播，定义为：1）新的HIV血清阳性（包括近期/急性 感染）; 2）先前已知的HIV血清阳性，具有可检测的病毒载量（在护理中或不在护理中）;和3）高危HIV 血清反应阴性这些人在风险环境中的收益是衡量 基于MHS的合作伙伴服务或其他网络招聘程序（即2步），将在P2 M中进行检查。 因此，P2 M旨在严格测试以下假设：（a）分析更接近HIV时间的分子簇数据 诊断将增加风险环境成员的产量;以及（B）使用先进的 我们团队创新的合作伙伴和网络服务方法也将提高产量， 公共卫生综合干预措施的有效性。然而，进行一系列的 随机试验来检验这些假设。然而，建模方法最适合于提供 当存在多种方法和可想象的条件时，背景并不适合于 研究试验，以及可能产生多种下游结果的地方。对于P2 M，这个多- 一个机构调查小组（来自休斯顿和芝加哥）将在现有艾滋病毒追踪合作的基础上， 受艾滋病毒影响最严重的人群和现有的基于病原体的艾滋病毒传播的观察队列 ABM模式，以指导和优先考虑干预措施。因此，我们的目标是：目标1）确定相对 与合作伙伴相比，针对HIV TRACE衍生分子簇的合作伙伴服务的有效性 目前提供的服务没有分子簇靶向（标准护理）来产生有风险的人 环境目标2）通过参数化一个基于代理的 利用现有的队列数据建立模型，根据以下因素得出风险环境中最佳的个体类别：1） 时间-分子簇鉴定; 2）分子簇数目和大小;以及3）实时与延迟 合作伙伴服务应对集群增长。目的3a）组合分子的相对有效性模型 监控和下一代合作伙伴服务-两步、三步和社交网络推荐-最佳收益 目标3b）探索模拟艾滋病毒预防干预措施（即：PrEP）及其 成本，因为它们是在现实世界的分子集群和合作伙伴/网络服务策略中建模的。

The Phylodynamics-targeted Partner service Models (P2M) project aims to guide and transform the rapidly evolving public health implementation of molecular HIV surveillance (MHS) based prevention interventions as a critical step towards HIV elimination. P2M seeks to study and optimize emerging new national guidance: The CDC's March 2017 recommendations of the HIV Cluster Guidance Working Group on health department implementation of MHS with clinical HIV resistance genotypes, now recommended at entry to care. These HIV-1 pol sequences can be used to identify “molecular clusters” of persons with genetically similar HIV. The overarching goal of MHS is to identify persons within the risk environment. These persons are potential sources, or recipients, of new HIV transmissions, and defined as: 1) new HIV seropositives (including recent/acute infections); 2) previously known HIV seropositives with detectable viral load (in care or not); and 3) at-risk HIV seronegatives. The yield of these persons in the risk environment is an important metric of the usefulness of MHS-based partner services or other network recruitment procedures (ie 2-step) that will be examined in P2M. Thus, P2M aims to rigorously test the hypotheses that: (a) analyzing molecular cluster data closer to time of HIV diagnosis will increase the yield of risk environment members; and (b) targeted prevention using advanced partner and network service approaches our team has innovated will also increase yield and improve effectiveness of public health combination interventions. It is not feasible, however, to conduct a series of randomized trials to test these hypotheses. Modeling approaches, however, are best suited for providing guidance when multiple approaches and conceivable conditions exist, the contexts do not lend themselves to research trials, and where multiple downstream outcomes are possible. For P2M, members of this multi- institutional investigative team (from Houston and Chicago) will build upon existing HIV TRACE collaborations, observational cohorts of populations most impacted by HIV and an existing Agent-Based HIV transmission model (ABM) to guide and prioritize interventions. Accordingly, we aim to: Aim 1) Determine the relative effectiveness of partner services targeted to HIV TRACE derived molecular clusters compared to partner services as currently delivered without molecular cluster targeting (standard of care) to yield persons in the risk environment. Aim 2) Optimize several cluster size and time thresholds by parameterizing an agent-based model with existing cohort data that best yield categories of individuals in the risk environment based upon: 1) time-to-molecular cluster identification; 2) molecular cluster number and size; and 3) real-time versus delayed partner services response to cluster growth. Aim 3a) Model relative effectiveness of combined molecular surveillance and next-generation partner services – 2-step, 3-step and social network referral – that best yield persons in the risk environment; Aim 3b) Explore simulated HIV prevention interventions (ie. PrEP) and their cost as they are modeled within real-world molecular cluster and partner/network service strategies.