Tracking and predicting the dynamics of HIV-1 epidemics in France using virus genomic data

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 partial sampling and to provide insights about epidemic history that even predates 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 temporal dynamics of two large clades that represent 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 circulating recombinant form CRF02 AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies. Author summaryDespite huge progress in prophylaxis and treatment, HIV epidemics remain a major public health issue in most countries. Therefore, understanding, tracking, and predicting epidemic dynamics is essential to design optimal prevention and screening strategies. A strong limitation is that most methods rely on occurrence data and are very sensitive to the unsampled portion of the epidemic (also known as the HIV hidden epidemic). To address this issue, we take advantage of phylodynamics methods that rely on viral sequence data. Thanks to data from the ANRS Primo cohort, we identify two epidemics present in France since the early 1980s that exhibit consistent, but some times different, dynamics. By simulating future scenarios, we demonstrate that the UNAIDS goal to reduce new HIV infections by 90 % from 2010 by 2030 is uncertain, at least for one of the two epidemics we consider. This is one of the first studies to leverage phylodynamic methods to analyse the French HIV epidemic. It also highlights how routinely-generated genomics data can enable detailed analyses that facilitate the design of efficient public health policies.