Leveraging public health genotyping databases for near real-time HIV surveillance

利用公共卫生基因分型数据库进行近乎实时的艾滋病毒监测

• 批准号: 10578672
• 负责人: Thomas K. Leitner
• 金额: $ 82.7万
• 依托单位: TRIAD NATIONAL SECURITY, LLC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578672
• 关键词: Age; Biological Markers; Clinical Data; Collaborations; Colorado; Coupled; Data; Databases; Development; Drug resistance; Engineering; Environment and Public Health; Epidemic; Epidemiology; Ethics; Generations; Genetic; Genotype; Goals; HIV; Individual; Infection; Information Networks; Interdisciplinary Study; International; Intervention; Investigation; Label; Laboratories; Legal; London; Maps; Markov Chains; Measures; Methodology; Methods; Michigan; Modeling; Molecular Disease; Monitor; Morphologic artifacts; Periodicals; Persons; Phylogenetic Analysis; Phylogeny; Population Surveillance; Prevention; Public Health; Quality Control; Recording of previous events; Reporting; Research Project Grants; Resolution; Resource Allocation; Resources; Risk; Sampling; Scientific Advances and Accomplishments; Services; Source; Speed; System; Testing; Time; United States Dept. of Health and Human Services; Update; Virus; bioinformatics tool; college; computational pipelines; data quality; design; epidemiologic data; experience; flexibility; follow-up; improved; innovation; prevent; reconstruction; resistance mutation; response; tool; transmission process; user-friendly

Summary In a collaboration between Los Alamos National Laboratory, Imperial College London, Colorado Department of Public Health and Environment, and Michigan Department of Health and Human Services, we propose to develop a near real-time surveillance tool leveraging existing databases to support public health efforts in testing, treatment, and prevention. Overall, we aim to develop a computational pipeline that will use data from a public health database and function as a practical surveillance tool. The pipeline's design will be modular to allow for customization and independent updating focusing on creating actionable surveillance reports in near real-time. Our approach is based on reconstructing the underlying transmission network that generated a particular HIV phylogeny. Such methods, aka. source attribution methods, have been shown to have less error, be less sensitive to sampling artifacts, provide more actionable information about how HIV spreads among age/risk/single or multiple-source connections, and to be able to identify unsampled persons, all better than simple genetic clustering methods. Thus, correct use of inferred transmission network information would improve resource allocation, allow more accurate and therefore faster interventions, and ultimately prevent more persons from becoming infected. We will also include data quality control measures and automatic checks for robustness and repeatability of the phylodynamic inferences. To achieve this, we divide the project into two aims: 1) Develop a near real-time surveillance tool for practical public health use, and 2) Develop phylodynamic analysis methods into modules that can be used to enhance the utility of the surveillance tool. We propose several innovative scientific advancements to the current state of the art of phylodynamic methodology and include aspects of quality control, robustness, and repeatability to solve the demanding computational tasks of integrating those methods into a surveillance tool. We carefully consider ethical and legal issues that may arise. We emphasize that inferred transmission network information is sensitive data that must be handled in the same responsible way as current partner services data. We will use data from Colorado and Michigan, both states with significant HIV epidemics, but with different demographic and epidemic situations, which has the advantage of forcing us to develop a flexible and universally meaningful surveillance system.

总结 在洛斯阿拉莫斯国家实验室、伦敦帝国理工学院、科罗拉多 公共卫生与环境部和密歇根州卫生与人类部 服务，我们建议开发一个利用现有数据库的近实时监控工具 支持公共卫生在检测、治疗和预防方面的努力。总的来说，我们的目标是发展一个 计算管道，将使用来自公共卫生数据库的数据，并作为一个实用的 监视工具。管道的设计将是模块化的，以允许定制和独立 更新重点是近实时创建可操作的监测报告。我们的做法是 基于重建产生特定艾滋病毒的潜在传播网络， 我是马根尼这样的方法，AKA。来源归属方法，已被证明有较少的错误， 对采样人为因素不那么敏感，提供更多关于艾滋病毒如何 在年龄/风险/单一或多源连接之间传播，并且能够识别 未抽样的人，都优于简单的遗传聚类方法。因此，正确使用 推断出的传输网络信息将改善资源分配，允许更准确地 从而更快地进行干预，最终防止更多的人受到感染。 我们还将包括数据质量控制措施和自动检查的鲁棒性， 可重复性的动态推理。为了实现这一目标，我们将该项目分为两个目标： 1)开发一种用于实际公共卫生用途的近实时监测工具，以及2）开发 将动态分析方法集成到模块中，可用于增强 监视工具。我们提出了几个创新的科学进步，以目前的状态， 动态方法学的艺术，包括质量控制、鲁棒性和 可重复性，以解决将这些方法集成到一个 监视工具。我们认真考虑可能出现的道德和法律的问题。我们强调 推断出的传输网络信息是敏感数据，必须以相同的方式处理， 负责的方式作为当前合作伙伴服务数据。我们将使用来自科罗拉多和密歇根的数据， 这两个州都有严重的艾滋病毒流行，但人口和流行情况不同， 它的好处是迫使我们发展一种灵活的、普遍意义的 监控系统