Predicting influenza evolution.

Seasonal influenza virus evolves to evade immune recognition, necessitating regular vaccine updates, a process that requires optimization by forecasting influenza evolution. Phylodynamic methods have successfully identified strains similar to dominant strains of the virus in upcoming influenza seasons. However, their capacity to predict allele frequency dynamics or fixation of mutations is very limited. This may result from considering virus evolution via phylogeny, simplifying the actual distribution of sequences in the population over time. Existing approaches focusing on single mutations do not account for the different genetic backgrounds, an essential feature of evolving populations. These simplifications undermine methods' predictability. To address this gap, we developed a physics-inspired method that accounts for the full evolutionary history and genetic background to estimate the fitness advantage of influenza mutations and predict strains that are most likely to be dominant in the future. We applied our approach to estimate the selective effects of mutations, investigate the nature of strongly selected mutations, and search for signals of vaccine-driven selection. Quantitative predictions from our research could facilitate informed vaccine update decisions and produce new data that could be integrated into other forecasting tools. We expect our methods could be extended to study other pathogens with similar modes of evolution.