| Autor: |
Golubchik, Tanya, Abeler-Dörner, Lucie, Hall, Matthew, Wymant, Chris, Bonsall, David, Macintyre-Cockett, George, Thomson, Laura, Baeten, Jared, Celum, Connie, Galiwango, Ronald, Kosloff, Barry, Limbada, Mohammed, Mujugira, Andrew, Mugo, Nelly, Gall, Astrid, Blanquart, François, Bakker, Margreet, Bezemer, Daniela, Ong, Swee Hoe, Albert, Jan, Bannert, Norbert, Fellay, Jacques, Gunsenheimer-Bartmeyer, Barbara, Günthard, Huldrych, Kivelä, Pia, Kouyos, Roger, Meyer, Laurence, Porter, Kholoud, van Sighem, Ard, van der Valk, Mark, Berkhout, Ben, Kellam, Paul, Cornelissen, Marion, Reiss, Peter, Ayles, Helen, Burns, David, Fidler, Sarah, Grabowski, Mary Kate, Hayes, Richard, Herbeck, Joshua, Kagaayi, Joseph, Kaleebu, Pontiano, Lingappa, Jairam, Ssemwanga, Deogratius, Eshleman, Susan, Cohen, Myron, Ratmann, Oliver, Laeyendecker, Oliver, Fraser, Christophe |
| Přispěvatelé: |
Blanquart, François, Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, On behalf of the HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE collaboration and the PANGEA consortium |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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