Speaker
Description
Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to sampling bias and to track the epidemic history even before the date of the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the history of two large clades that reflect key features of the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. Finally, using numerical simulations, we investigate prospective scenarios for future epidemic trends. This study is one of the first to analyse the HIV epidemic in France using phylodynamics methods. It demonstrates the historical and public health value of routine HIV sequence data in epidemiological surveillance.
