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

点击翻译按钮获取中文摘要

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.