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

使用新颖的系统动力学和深度学习框架预测艾滋病毒传播网络的轨迹

• 批准号: 10155407
• 负责人: Mattia Prosperi
• 金额: $ 70.51万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10155407
• 关键词: Adopted; Architecture; Area; Artificial Intelligence; Automobile Driving; Behavioral; Case Study; Clinical; Clinical Data; Collaborations; Communities; Complex; Coupled; Coupling; Data; Databases; Diagnosis; Ensure; Epidemic; Evaluation Reports; Evaluation Studies; Florida; Focus Groups; Future; Goals; Growth; Guidelines; HIV; HIV Infections; HIV diagnosis; Health; Incidence; Individual; Infection; Intervention; Link; Location; Maps; Methods; Modeling; Molecular; Molecular Epidemiology; Names; Network-based; Patients; Pattern; Persons; Population; Populations at Risk; Prevalence; Prevention; Prevention Measures; Prevention strategy; Procedures; Public Health; Questionnaires; Recording of previous events; Research; Risk; Risk Behaviors; Secure; Services; Structure; Surveillance Program; System; Technology; Training; Translating; Trust; Update; Viral; Virus; antiretroviral therapy; base; complex data; computer framework; data exchange; deep learning; deep learning algorithm; design; dynamical evolution; effectiveness evaluation; innovation; intelligent algorithm; meetings; migration; novel; peer; preventive intervention; public health intervention; public health relevance; social; sociodemographic factors; sociodemographics; spatiotemporal; tool; transmission process; trend; usability; viral transmission; working group

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.

概括 尽管联合抗逆转录病毒疗法出现了，但持续的艾滋病毒流行仍然违反预防和 干预策略旨在大大降低全球流行和发病率。为了 实现2020年UNAIDS 90-90-90目标（诊断为艾滋病毒的患者中有90％，有90％的人被诊断出 要接受持续的抗逆转录病毒治疗，有90％的人接受病毒抑制），这是 开发可用于预测本地化的增长和轨迹的创新工具所必需的 由特定的传输簇驱动的次发育。系统动力学分析已广泛用于 在本地，区域和全球的艾滋病毒领域跟踪起源并重建病毒人群历史 等级。但是，此类研究仅是回顾性的，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创新 计算框架耦合系统动力学推断和行为网络数据与人工 智能算法能够预测艾滋病毒传播簇未来轨迹并通知密钥 新感染的决定因素。我们建议通过执行三个特定目标来实现这一目标：1。 一个基于系统动力学的骄傲模块，以预测HIV感染热点[感染]； 2。发​​展行为 基于网络的骄傲模块，用于艾滋病毒感染的风险[未感染]，3。 将新的骄傲预测技术部署到公共卫生中，并通过 同行更改代理模型。特别是，通过与佛罗里达卫生部的密切合作伙伴关系 （fldoh），我们将分析FLDOH过去十二年中FLDOH组装的现有数据库 包括广泛的HIV分子序列，临床和行为网络数据。佛罗里达有艾滋病毒案 2016年，每10万人的24.0人的汇率，目前是美国的第三个州 发病率。我们与FLDOH的合作伙伴关系将确保拟议研究的结果用于 通过优化基于公共卫生的监视计划，告知有针对性的人，减少艾滋病毒流行 干预策略，并实施更有效的预防措施。