In this project, our objective is to study the influenza virus with the goal of making predictions that can guide strategies for managing the epidemic. We plan to characterize influenza from the perspective of molecular evolution and its interaction with the human immune system, also known as antigenic evolution. We aim to establish connections between these two processes. Viewing antigenic evolution through a clustering lens could potentially lead to improved predictions about the dominant strains in the upcoming year. The project has a significant methodological component, which involves developing suitable statistical techniques to handle high-dimensional data sets with complex dependency structures. Methodologically, we focus on two main points: firstly, we aim to develop a methodology based on U-statistics for inference in clustering problems. Secondly, we plan to develop Bayesian phylodynamic methods that explicitly model the relationship between molecular and phenotypic evolution, such as antigenic evolution.