Development and validation of regional models of HIV vulnerabilities and solutions

项目总结 为了实现关键的健康里程碑(例如，国家艾滋病毒/艾滋病战略1)，公共卫生系统需要 方法预测一个地区内的艾滋病流行情况。佛罗里达州迈阿密新诊断病例意外激增 如果公共卫生官员能够预测这些变化并进行干预，那么很可能避免发生在印第安纳州的奥斯汀 期待。然而，作为帮助公共卫生的主流工具，建模方法没有得到充分利用 决定，2由于障碍，包括：(A)没有考虑到 艾滋病毒传播动态预测因素之间的时空关系，(B)缺乏强大的BIG 社交媒体数据，以衡量有关艾滋病毒检测和预防的人口规范和信息传播 服务，(C)缺乏分散但相关的数据来源，以预测艾滋病毒流行病学，(D)缺乏 这种整合结果的可视化工具，以及(E)缺乏模型来衡量新的 干预措施(例如，艾滋病毒疫苗)或当前干预措施的变化。在此应用程序中，我们建议 如果成功，将允许公共卫生官员和科学界对这种 预测，从而规划干预措施，如PrEP(暴露前预防)。该项目将依赖于 关于现有但分散的区域流行病、社会结构、社交媒体和干预来源 产生模型和网络地理信息系统艾滋病毒的数据，这是公共卫生官员和研究人员可以使用的工具。 该工具将分析数据并在综合输出中产生结果，以确定易受影响的区域，以及 预测未来的脆弱性和干预政策变化的影响。我们将整合 卫生部门记录的流行病学和生物医学服务数据，来自美国人口普查局的数据， 美国社区调查、美国男性互联网调查、传输网络数据集、社交媒体 数据，以及来自新干预措施的影响大小，以得出预测。我们还将开发新的方法来 社交媒体分析和比较时空建模技术。该系统将提供 关于邮政编码、县和地区的服务分配的建议，设置为引入服务 同样，在各个地区，或者针对将为整个州带来最大改善的地区。这个 伊利诺伊大学、埃默里大学和奥尔巴尼大学提供理想的社会科学、公共卫生、 以及这个项目的计算基础设施。球队(伊利诺伊州：阿尔巴拉辛、陈、李、桑达拉姆和王建民； 奥尔巴尼：霍尔特格雷夫)开发了预测艾滋病毒和流感的尖端大数据模型，以及原创的 时空分析和现有最先进的网络地理信息系统工具。埃默里大学的杜博士曾在 在疾控中心从事艾滋病毒监测流行病学工作20年，现在是一名教员。此外，健康 该部人员将参与设计和测试网络地理信息系统-艾滋病毒 项目，如果方法通过了一组预先建立的进行/不进行标准。

PROJECT SUMMARY To achieve critical health milestones (e.g., National HIV/AIDS Strategy1), the public health system needs methods to predict HIV epidemiology within a region. An unexpected surge of new diagnoses in Miami, FL or Austin, IN, may well be avoided if public health officials are able to forecast these changes and to intervene in anticipation. However, modeling approaches are underutilized as mainstream tools to aid public health decisions,2 owing to barriers including (a) unavailability of user-friendly methods that consider the spatiotemporal relations among predictors of HIV transmission dynamics, (b) lack of inclusion of powerful big social media data to gauge population norms and diffusion of information about HIV testing and prevention services, (c) lack of integration of disperse yet relevant sources of data to predict HIV epidemiology, (d) lack of visualization tools for the results of that integration, and (e) lack of models to gauge impact of new interventions (e.g., an HIV vaccine), or changes in current interventions. In this application, we propose methods that, if successful, will allow public health officials and the scientific community to make such refined predictions and thereby to plan for interventions such as PrEP (PreExposure Prophylaxis). The project will rely on existing but disperse sources of regional epidemiological, socio-structural, social media, and intervention data to produce models and Cyber-GIS-HIV, a tool that can be used by public health officials and researchers. The tool will analyze data and produce results in an integrated output identifying vulnerable regions, and predicting future pockets of vulnerability and the effects of changes in intervention policy. We will integrate epidemiological and biomedical service data recorded by health departments, data from the US Census, the American Community Survey, the American Men Internet Survey, transmission network datasets, social media data, and effect sizes from new interventions to derive predictions. We will also develop new methods for social media analyses and compare spatio-temporal modeling techniques. The system will offer recommendations about service allocation for a zip code, a county, and a region, set to introduce services equally across areas, or to target the areas that would give the most improvement for the state as a whole. The University of Illinois, Emory University, and the University at Albany offer the ideal social science, public health, and computing infrastructure for this project. The team (Illinois: Albarracin, Chan, Li, Sundaram, and Wang; Albany: Holtgrave) has developed cutting-edge big-data models to predict HIV and flu, as well as original spatiotemporal analysis and existing state-of-the-art CyberGIS tools. Dr. Do at Emory served in the division of HIV surveillance epidemiology at CDC for two decades and is now a faculty member. In addition, health department personnel will be involved in designing and in testing CyberGIS-HIV during the last year of the project, if the methods pass a preestablished set of Go/No Go criteria.