Huang 2024 used a pseudovirus platform to evaluate the alignment between the 2023 and 2024 seasonal influenza vaccine H3N2 strain (A/Darwin/6/2021) and currently circulating influenza strains.
Harvey 2023 developed a new approach using a Bayesian model for integrating genetic and antigenic data to identify genetic changes in H3N2 virus that underpin antigenic drift.
Peng 2023 developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. Results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy.
The US Centers for Disease Control and Prevention is characterizing NA antigenicity of A(H3N2) viruses, which includes optimizing methods for analysis and assessing contemporary A(H3N2) viruses as part of the WHO vaccine recommendation process.