On the analysis of antigenic relatedness of influenza A (H3N2) viruses

An accurate assessment of antigenic relatedness between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic relatedness measure that account for specific changes in strains that are of epidemiological importance in influenza. A computational model was developed using distinguishing features of antigenic variants to analyze antigenic relatedness among influenza strains. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. It was found that all model parameters were determinants of antigenic relatedness between strains and that the model accurately predicts the antigenic relatedness between influenza A (H3N2) viruses. The methods presented in this study will potentially complement the global efforts in influenza surveillance.