Integrating within-host genetic diversity and spatiotemporal data in phylogenetic reconstructions of RNA virus transmission

Viral diseases represent important threats, not only to public health but also to global and regional socio-economic networks. Their effective prevention and mitigation can only be achieved by understanding viral transmission dynamics. A critical tool in the hand of researchers and public health workers to achieve this goal is phylogenetic reconstruction, which is based on viral genetic information and allows the probabilistic reconstruction of evolutionary relationships between viral isolates, in a diagram that resembles a “family tree”. Phylogenetics traditionally resort to consensus sequences, where for each isolate only the main nucleotidic variant at each position is used, without any consideration for co-existing minor variants. However, on short timescale during epidemics, or when considering samples directly linked by a transmission chain, insufficient divergence may accumulate between isolates to allow robust phylogenetic reconstructions. Combining several sources of information in an advance statistical framework can help structure and strengthen phylogenetic reconstructions, like stacks supporting tree branches. In this post-doctoral project, I aim to advance these data integration efforts by incorporating additional information sources (intra-host genetic diversity, time and space), evaluating how they shape phylogenetic reconstructions, and under which modalities this leads to more accurate reconstructions and insights in evolutionary and transmission dynamics.