Integrating spatial heterogeneity with temporal and demographic inferences, with an application to combating human rabies

Genomic analyses have revealed critical insights into the evolution and spread of pathogens, with spatial diffusion models having been essential to develop a field of research known as phylogeography, where phylogenetic trees are annotated with spatial locations. These diffusion models are however relatively simple, as they do not take into account environmental features when modelling the geographic spread of a pathogen. In this research proposal, we aim to develop new spatial diffusion models that accommodate spatial heterogeneity to improve phylogenetic reconstruction accuracy and realism. To this end, we will build on the time-measured phylogenetic methodology in the BEAST framework and adopt state-of-the-art statistical and computational techniques to perform efficient estimation of the evolution and spread of pathogens. To visualize the outcome of our reconstruction procedures, as well as to identify potential cases where environmental features may impact pathogen spread, we will develop a user-friendly and flexible web-based visualization platform that can easily be applied to different pathogens. We anticipate that this research will be essential to increase our understanding of the key drivers of pathogen spread and plan on exploiting our findings to develop a framework that allows proposing and evaluating potential intervention strategies to impact the spread of rabies virus, and by extension other viruses of interest.