Quantifying HIV Transmission Flow from Cross-Sectional Viral Phylogenetic Deep Sequence Data: A Population-Based Study in Rakai, Uganda

• Activity Number: 534 - Bayesian Inference with Complex Biomedical Systems
• Type: Invited
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: International Society for Bayesian Analysis (ISBA)
• Author(s): O. Ratmann* and X. Xi and S. E. F. Spencer and Joseph Kagaayi and M. Kate Grabowski and on behalf of and Matthew Hall
• Companies: Imperial College London and Imperial College London and University of Warwick and Rakai Health Sciences Program and Johns Hopkins School of Medicine and Rakai Health Sciences Program and PANGEA-HIV and Big Data Institute, University of Oxford
• Keywords: public health; phylogenetics; Gaussian process; Poisson trick; MCMC; flow

Abstract

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. However, it is often unclear who and where these “source” populations are. Viral deep-sequence data have recently been established as a reliable source of information for inferring the direction of transmission at an accuracy sufficient for population-level analyses. We present a novel semi-parametric Bayesian modelling framework of viral sequence data to quantify HIV transmission flows and detect source and sink population groups that directly incorporates this information. Our model uses a high-dimensional set of Poisson regression equations, adjusting for sampling heterogeneity known from cross-sectional surveillance to avoid selection bias in the flow estimates. We illustrate the methodology on population-based sequence data obtained from infected individuals participating in the Rakai Community Cohort Study in south-eastern Uganda, indicating that the method provides new opportunities for identifying the drivers of HIV spread.