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: 10357920
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10357920
• 关键词: Biological Markers; Clinical Data; Collaborations; Colorado; Coupled; Custom; 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; Markov Chains; Measures; Methodology; Methods; Michigan; Modeling; Molecular Disease; Monitor; Morphologic artifacts; Periodicity; 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; Services; Source; Speed; System; Testing; Time; United States Dept. of Health and Human Services; Update; Virus; base; 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