Missing clusters in 20K HIV genomes from four African countries and the future of HIV prevention

ABOUT THE SPEAKER
Francesco Di Lauro is a modeller with a background in Theoretical Physics and a PhD in Applied Maths. During his PhD at the Department of Mathematics of the University of Sussex, he worked on models of epidemic spreading on networks, with a focus on inference of network and epidemic properties from population-level data. After his PhD, he joined the Big Data Institute at the University of Oxford as a postdoctoral researcher, specifically the Pathogen Dynamics group. He is currently working on HIV epidemic spreading in Sub-Saharan Africa and on digital contact tracing. His research is primarily focussed in understanding how sexually transmitted infections propagate through a population, using tools from both genomics and network science.

ABSTRACT
New HIV infections have been steadily declining across sub-Saharan Africa, leading UNAIDS to declare that, with current progress maintained, AIDS could cease to be a public health threat by 2030. In the Global North, HIV prevention is increasingly centred around rapid genetic analysis of transmission chains and outbreak control. To explore whether similar approaches could be used in high-prevalence epidemics in Africa, we compared the viral genetic structure of such epidemics to those in Europe, and to three mathematical models of generalised epidemics. In 19,968 genomes from four African countries, we found no large clusters of closely genetically linked viruses, suggesting that more generalised public health approaches are needed.