Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

Significance Humans mount an antibody-mediated immune response against influenza viruses that can be recalled. Nevertheless, individuals can suffer from recurrent influenza infections as viruses can change their antigenic properties by altering their surface glycoproteins. This antigenic evolution requires frequent update of seasonal influenza vaccines. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System assess the antigenic phenotypes of circulating viruses. Based on the relationship of antigenic distance to genetic differences between viruses, we developed a model to interpret measured antigenic data and predict the properties of viruses that have not been characterized antigenically and explore the model’s value in predicting the future composition of influenza virus populations. Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.