Data from: Sequence-based detection of emerging antigenically novel influenza A viruses

The detection of evolutionary transitions in influenza A (H3N2)
viruses' antigenicity is a major obstacle to effective vaccine design
and development. In this study, we describe NIAViD, an unsupervised
machine learning tool, adept at identifying these transitions, using HA1
sequence and associated physicochemical properties. NIAViD, performed with
88.9% (95% CI, 56.5%–98.0%) and 72.7% (95% CI,43.4%– 90.3%) sensitivity in
training and validation respectively, outperforming the uncalibrated null
model – 33.3% (95% CI,12.1%–64.6%) and does not require the need for
potentially biased, time-consuming and costly laboratory assays. The
pivotal role of Boman’s index, indicative of the virus’s cell surface
binding potential, is underscored, enhancing the precision of detecting
antigenic transitions. NIAViD's efficacy is not only in identifying
influenza isolates that belong to novel antigenic clusters, but also in
pinpointing potential sites driving significant antigenic changes, without
the reliance on explicit modeling of hemagglutinin inhibition titers. Our
approach holds immense promise to augment existing surveillance networks,
offering timely insights for the development of updated, effective
influenza vaccines. Consequently, NIAViD, in conjunction with other
resources, could be used to support surveillance efforts and inform the
development of updated influenza vaccines.