The emergence of rapidly spreading viral variants (such as VOCs for COVID-19) can be decisive in the progression of epidemics, leading to new waves of infection and significant changes in viral populations. Phylodynamic methods can help identify and study the initial transmission dynamics of these variants. These methods exploit the fact that changes in virus population dynamics leave a phylogenetic signal to estimate evolutionary parameters. However, these methods assume that lineages are independent conditioning and, therefore, can only use local information to estimate the lineage’s dispersal potential. In this project, we postulate that the emergence of a new, rapidly dispersing lineage leaves a signature in the evolutionary history of all lineages, infecting the same population.

Furthermore, we postulate that this effect can be used to predict which lineages will become dominant. To this end, we propose adapting a new phylodynamic machine learning approach to the problem of identifying and tracking these variants. The machine learning approach will allow us to take advantage of the large number of molecular sequences available for these viruses, the analysis of which would be prohibitive for traditional phylodynamic methods. Furthermore, by better using global information, these methods may enable early detection of these variants, potentially even before substantial sampling of the emerging variant.