| dc.contributor.author |
Tanya Golubchik, Lucie Abeler-Dörner , Matthew Hall Chris Wymant , David Bonsall , George Macintyre-Cockett , Laura Thomson , Jared M Baeten Connie L Celum , Ronald M Galiwango , Barry Kosloff Mohammed Limbada , Andrew Mujugira , Nelly R 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 N Burns , Sarah Fidler Mary 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 |
|
| dc.description.abstract |
Background: Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention.
Results: We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly 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 performs equally well for all major HIV subtypes based on data from African and European cohorts.
Conclusions: We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level. |
en_US |