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.

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