Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework

摘要 尽管出现了联合抗逆转录病毒疗法，但目前的艾滋病毒流行仍然无法预防和 旨在显著降低全球发病率和发病率的干预战略。为了 实现2020年联合国艾滋病规划署90-90-90目标(90%的艾滋病毒携带者确诊，90%的人确诊 持续接受抗逆转录病毒治疗，90%的人接受治疗以维持病毒抑制)，这是 有必要开发可用于预测本地化的增长和轨迹的创新工具 由特定传播群群驱动的亚流行病。系统动力学分析已被广泛应用于 艾滋病毒领域，以追踪病毒来源并重建当地、区域和全球的病毒人口历史 水平。然而，到目前为止，这样的研究只是回溯性的，几乎没有做出预测的能力。 关于未来的流行趋势。该项目的总体目标是开发一种创新的 将系统动力学推理和行为网络数据与人工智能相结合的计算框架 能够预测HIV传播集群未来轨迹并告知关键信息的智能算法 新感染的决定因素。我们建议通过实现三个具体目标来实现这一目标：1.发展 基于系统动力学的PROID模块用于预测HIV感染热点；2.开发行为模型 以网络为基础的艾滋病毒感染风险[未感染人群]自豪感模块，以及3.为 将新的PARE预测技术部署到公共卫生中，并通过 对等变更代理模型。特别是，通过与佛罗里达州卫生部的密切合作， (FLDoH)，我们将分析FLDoH在过去12年中收集的现有数据库 包括广泛的HIV分子序列、临床和行为网络数据。佛罗里达州有一例艾滋病毒病例 2016年，该州每10万人中有24.0人，目前按年计算是美国第三个州 发病率。我们与FLDoH的伙伴关系将确保拟议的研究结果将用于 通过优化基于公共卫生的监测计划来遏制艾滋病毒流行，向有针对性的 干预策略，并实施更有效的预防措施。

SUMMARY Despite the advent of combined antiretroviral therapy, the ongoing HIV epidemic still defies prevention and intervention strategies designed to reduce significantly both prevalence and incidence worldwide. In order to achieve the 2020 UNAIDS 90-90-90 goal (90% of people living with HIV diagnosed, 90% of people diagnosed to be on sustained antiretroviral treatment, and 90% of people on treatment to maintain viral suppression), it is necessary to develop innovative tools that can be used for predicting the growth and trajectory of localized sub-epidemics driven by specific transmission clusters. Phylodynamic analysis has extensively been used in the HIV field to track the origin and reconstruct the virus demographic history both at local, regional and global level. However, such studies have been so far only retrospective, with little or no power to make predictions about future epidemic trends. The overarching goal of the prosed project is to develop an innovative computational framework coupling phylodynamic inference and behavioral network data with artificial intelligence algorithms capable of predicting HIV transmission clusters future trajectory, and informing on key determinants of new infections. We propose to achieve this goal by carrying out three specific aims: 1. Develop a phylodynamic-based PRIDE module to forecast HIV infection hotspots [the infected]; 2. Develop a behavioral network-based PRIDE module for risk of HIV infection [the uninfected], and 3. Carry out focus groups for deploying the new PRIDE forecasting technology into public health, and implement prevention through the peer change agent model. In particular, through a close partnership with the Florida Department of Health (FLDoH), we will analyze existing databases that the FLDoH has assembled over the past twelve years including extensive HIV molecular sequence, clinical and behavioral network data. Florida had an HIV case rate of 24.0 per 100,000 people in 2016, and it is currently the third state in the USA in terms of yearly incidence. Our partnership with the FLDoH will ensure that the results of the proposed research will be used to curtail the HIV epidemic by optimizing public health based surveillance programs, informing targeted intervention strategies, and implementing more effective prevention measures.