HIV-phyloTSI: Subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data

作者：Tanya Golubchik, Lucie Abeler‐Dörner, Matthew Hall, Chris Wymant, David Bonsall, George MacIntyre-Cockett, Laura Thomson, Jared M. Baeten, Connie Celum, Ronald M. Galiwango, Barry Kosloff, Mohammed Limbada, Andrew Mujugira, Nelly Mugo, Astrid Gall, François Blanquart, Margreet Bakker, Daniela Bezemer, Swee Hoe Ong, Jan Albert, Norbert Bannert, Jacques Fellay, Barbara Gunsenheimer‐Bartmeyer, Huldrych F. Günthard, Pia Kivelä, Roger D. Kouyos, Laurence Meyer, Kholoud Porter, Ard van Sighem, Mark van der Valk, Ben Berkhout, Paul Kellam, Marion Cornelissen, Peter Reiss, Helen Ayles, David Burns, Sarah Fidler, M. Kate Grabowski, Richard Hayes, Joshua T. Herbeck, Joseph Kagaayi, Pontiano Kaleebu, Jairam R. Lingappa, Deogratius Ssemwanga, Susan H. Eshleman, Myron S. Cohen, Oliver Ratmann, Oliver Laeyendecker, Christophe Fraser · 发表于：medRxiv · 年份：2022 · DOI：10.1101/2022.05.15.22275117 · 被引用次数：9 · 研究领域：HIV Research and Treatment、HIV/AIDS Research and Interventions、HIV, Drug Use, Sexual Risk

Abstract Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining duration of infection classify samples into recent and non-recent and are unable to give more granular TSI estimates. These binary classifications have a limited recency time window of several months, therefore requiring large sample sizes, and cannot assess the cumulative impact of an intervention. We developed a Random Forest Regression model, HIV-phyloTSI, that combines measures of within-host diversity and divergence to generate TSI estimates from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. It performed equally well for all major HIV subtypes based on data from African and European cohorts. We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.