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

摘要 在科罗拉多州伦敦帝国理工学院洛斯阿拉莫斯国家实验室 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乎实时的监控工具 支持公共卫生在检测、治疗和预防方面的努力。总括而言，我们的目标是发展一套 计算管道，将使用来自公共卫生数据库的数据，并作为实用的 监控工具。管道的设计将是模块化的，以允许定制和独立 更新侧重于近乎实时地创建可操作的监视报告。我们的方法是 基于重建产生特定艾滋病毒的底层传播网络 系统发育学。这样的方法，也就是。源归因方法已被证明具有较小的误差， 对样本文物不那么敏感，提供更多关于艾滋病毒如何 在年龄/风险/单一或多个来源的联系中扩散，并能够识别 未抽样者，均优于简单的遗传聚类法。因此，正确使用 推断的传输网络信息将改进资源分配，允许更准确 从而更快地进行干预，并最终防止更多的人感染。 我们还将包括数据质量控制措施和健壮性的自动检查以及 系统动力学推论的重复性。为了实现这一目标，我们将该项目分为两个目标： 1)开发用于实际公共卫生的近乎实时的监测工具，以及2)开发 将系统动力学分析方法转换为模块，可用于增强 监控工具。对于目前的状态，我们提出了几项创新的科学进展 系统动力学方法论的艺术，包括质量控制、稳健性和 可重复性，以解决将这些方法集成到 监控工具。我们仔细考虑可能出现的道德和法律问题。我们强调 推断的传输网络信息是必须在同一网络中处理的敏感数据 负责任的方式作为当前合作伙伴服务数据。我们将使用科罗拉多州和密歇根州的数据， 这两个州都有严重的艾滋病毒流行，但人口和疫情情况不同， 它的好处是迫使我们开发出一种灵活的、具有普遍意义的 监控系统。

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.