Mapping and predicting HIV-transmission hotspots with phylogenetics and geospatial machine learning

利用系统发育学和地理空间机器学习绘制和预测 HIV 传播热点

• 批准号: 10267558
• 负责人: David Epstein
• 金额: $ 139.09万
• 依托单位: NATIONAL INSTITUTE ON DRUG ABUSE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10267558
• 关键词: AIDS prevention; Algorithms; Area; Baltimore; Big Data; Cities; Color; Data; Databases; Epidemiologist; Epidemiology; Extramural Activities; Future; Geographic Locations; Goals; Government Agencies; HIV; Hour; Human immunodeficiency virus test; Imagery; Individual; Legal patent; Local Government; Machine Learning; Manuscripts; Maps; Methods; Modeling; Outpatients; Participant; Patient Recruitments; Patients; Persons; Phylogenetic Analysis; Population; Postdoctoral Fellow; Preparation; Prevention; Process; Publications; Research; Resources; Risk; Stress; Surface; System; Taxes; Techniques; Time; Viral; Work; data space; drug craving; mHealth; men who have sex with men; method development; outreach; peer; pre-exposure prophylaxis; prediction algorithm; social; social deficits; success; technique development; transmission process

With this project, we are bringing something very new to HIV tracking and prevention, using two different set of big-data techniques in which we are already ahead of our peers. The first set of techniques is geospatial: we are validating new ways to interpolate physical/social-disorder data between directly observed city blockface to regions not directly observed. We use full-land-coverage data (e.g., satellite imagery, tax data) to account for spatial discontinuities such as parks and major roads. We are working on ways to extrapolate beyond the outer spatial boundaries of the regions where observations have been made. We have a methods manuscript in preparation, and our success has already elicited great enthusiasm from local governmental agencies that have agreed to provide environmental data and assist in participant recruitment. The second set of techniques is temporal: we are validating new ways to generate live predictions of outpatients imminent risk of drug craving or stress several hours into the future. We do this in machine-learning models that use several hours of the patients GPS tracks in combination with person-level information. We have a provisional patent for this process ("Method and System for a Mobile Health Platform," PCT/US2016/029553) and we are finishing a manuscript for publication. Together, these techniques can be applied to create a proactive epidemiological approach to HIV prevention. The future-prediction algorithms that we use on a time scale of hours for individuals will be adapted to work on a time scale of days, weeks, or month for areas and social venues that become hotspots for HIV transmission. Using viral phylogenetic data, social-contact data, and activity-space data, we intend to develop wall-to-wall surface maps of HIV reservoir and transmission risk in the city of Baltimore, develop algorithms for prediction of changes in the maps, and use viral phylogenetic data to tailor our algorithms for specific key populations such as MSM of color We have collected data from research participants to provide some of the empirical grounding for this project. One of our postdocs is working with an extramural HIV epidemiologist, who has access to city and state databases that will provide input to our machine-learning models. The ultimate goal of the project will be to help focus PrEP and other prevention resources into the geographical areas where they are about to become most needed, using strategies that are predictive rather than reactive.

通过这个项目，我们使用了两套不同的大数据技术，为艾滋病毒跟踪和预防带来了非常新的东西，我们在这方面已经领先于我们的同行。 第一套技术是地理空间：我们正在验证在直接观察到的城市街区和没有直接观察到的区域之间插入物理/社会混乱数据的新方法。我们使用全土地覆盖数据(例如，卫星图像、税收数据)来考虑公园和主要道路等空间不连续性。我们正在研究如何在已进行观测的区域的外部空间边界之外进行推断。我们正在准备一份方法手稿，我们的成功已经引起了当地政府机构的极大热情，他们同意提供环境数据并协助招募参与者。 第二套技术是暂时的：我们正在验证新的方法，以生成对门诊患者迫在眉睫的药物渴求风险或未来几个小时后压力的实时预测。我们在机器学习模型中做到了这一点，该模型使用几个小时的患者GPS跟踪结合个人水平的信息。我们拥有这一过程的临时专利(“移动健康平台的方法和系统”，PCT/US2016/029553)，我们正在完成出版的手稿。 综合起来，这些技术可以用来创造一种预防艾滋病毒的积极的流行病学方法。我们在几小时的时间尺度上为个人使用的未来预测算法将被调整为在几天、几周或几个月的时间尺度上工作，适用于成为艾滋病毒传播热点的地区和社交场所。利用病毒系统学数据、社会接触数据和活动空间数据，我们打算开发巴尔的摩市艾滋病毒宿主和传播风险的壁到壁表面图，开发地图变化的预测算法，并使用病毒系统学数据为特定的关键人群定制算法，如有色人种男男性接触者 我们从研究参与者那里收集了数据，为这个项目提供了一些经验基础。我们的一名博士后正在与一位校外艾滋病毒流行病学家合作，他可以访问城市和州的数据库，这些数据库将为我们的机器学习模型提供输入。 该项目的最终目标将是使用预测性而不是反应性的战略，帮助将预防预案和其他预防资源集中到即将成为最需要的地理区域。