Next-generation algorithms using multiple biomarkers for precise estimation of HIV infection duration and population level incidence

使用多种生物标志物的下一代算法精确估计 HIV 感染持续时间和人群水平发病率

• 批准号: 10399653
• 负责人: Thomas K. Leitner
• 金额: $ 70.44万
• 依托单位: TRIAD NATIONAL SECURITY, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-03 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10399653
• 关键词: AIDS prevention; Age; Algorithms; Antiviral Therapy; Avidity; Bayesian Method; Biological Assay; Biological Markers; Classification; Community Health; Confounding Factors (Epidemiology); Contact Tracing; Cross-Sectional Studies; DNA; Data; Data Set; Databases; Development; Diagnosis; Dideoxy Chain Termination DNA Sequencing; Evaluation; Gender; Genetic; Genetic Polymorphism; Genetic Recombination; Genetic Variation; Goals; HIV; HIV Infections; HIV Seropositivity; Human; Human immunodeficiency virus test; Immunologic Markers; Incidence; Individual; Infection; Laboratories; Measurement; Measures; Methodology; Methods; Modeling; Outcome; Patients; Persons; Population; Prevention; Prevention program; Probability; Protocols documentation; Public Health; Publishing; Research; Role; Sampling; South Africa; Statistical Models; Structure; Subgroup; Surveys; Sweden; System; Time; Training; Uganda; Uncertainty; Validation; Variant; Viral; Work; Zimbabwe; base; cohort; data streams; flexibility; high risk; improved; next generation; next generation sequencing; patient variability; serological marker; study population; surveillance data; time use; tool; user-friendly; web site

SUMMARY Given the central role of HIV incidence estimation in both surveillance and evaluation of HIV prevention programs, it is essential to have reliable, inexpensive, and usable methods for quickly estimating incidence in near-real time. This proposal aims to advance that goal by capitalizing on our recent developments in using a spectrum of biomarker data and HIV surveillance data to compute the complete posterior probability distribution of the time since infection for recently diagnosed persons. We posit that such estimates contain not only more accurate information about time of infection than standard binary classification (recent/long- term), but that it also gives realistic confidence bounds on HIV incidence estimates as it appropriately takes biomarker measurements and patient variation into account. We further posit that an accurate HIV incidence estimate must take three components into account: 1) The multi-assay algorithm (MAA)-adjusted time of infection (rather than the date of diagnosis or simple recent/non-recent binary classification); 2) The number of undiagnosed persons living with HIV (PLHIV) at a certain time (which may be diagnosed later); and 3) The number of HIV positive individuals that move into the study population. Specifically, we will 1) Develop laboratory protocols and algorithms for measuring and modeling individual biomarkers for probabilistic estimation of time of HIV infection; 2) Combine biomarkers into a generalized MAA for improved estimation of time of HIV infection and HIV incidence; and 3) Apply new MAA to determine the duration of infection and estimate HIV incidence in different human populations. Using large data, in total >13,000 patients with up to 7 different biomarkers determined, from Sweden, USA, South Africa and Zimbabwe, we will establish longitudinal and single-time point training data, as well as validation data, to develop validated, publicly available, methods to estimate: 1) the full distribution of time since infection for individual PLHIV; and 2) population- level HIV incidence based on either study-based samples or existing surveillance systems, and changes thereof over time, using new biomarkers, algorithms and models.

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