Cai 2024 developed the FluPMT model that integrates virus mutation temporal information and antigenic information using multi-task learning to predict the predominant strains of the following year and identify key residue-level factors driving viral evolution in HA sequences. Use of the model on two influenza datasets demonstrated the predictive performance of FluPMT.
Catani 2024 examined how NA accumulates mutations and evaluated how these mutations affect immune responses; analyzed the antigenic diversity of a panel of N2 NAs derived from H3N2 viruses circulating between 2009 and 2017, identifying at least four major phylogenetic groups; determined that amino acid residues in N2 NA near the catalytic site have a major impact on NAI susceptibility by immune sera; and developed a machine learning method to predict the impact of mutations in the N2 NA panel on NAI.
Shah 2024 developed a machine learning model to predict (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their HA1 sequences and associated metadata. The model distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change.
Hayati 2023 trained machine-learning models to predict which sequences are most likely to grow during the upcoming influenza season based on features of phylogenetic trees and explored choices for sequences to be considered for inclusion in the following year’s seasonal influenza vaccine.
Lee 2023 created an interactive visualization tool to inform the analysis of influenza virus evolution by displaying serologic data in a phylogenetic context, which enables direct comparison of antigenic distances between vaccine candidates.
Liu 2023 developed PREDAC-H1pdm, a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 strains.
Perofsky 2023 compared experimental and sequence-based measures of A(H3N2) evolution in predicting regional epidemic dynamics in the United States across 22 seasons, from 1997 to 2019, and considered the effects of other co-circulating influenza viruses, prior immunity, and vaccine-related parameters, such as coverage and effectiveness, on A(H3N2) incidence. Results indicated that evolution in HA and NA contributes to variability in epidemic magnitude across seasons, though viral fitness appears to be secondary to subtype interference in shaping annual outbreaks.
Thadani 2023 developed EVEscape, a computational method for predicting the likelihood of antibody escape for viral mutations, including influenza HA mutations.
NIAID CEIRR is supporting research on the evolutionary dynamics of influenza to predict evolutionary trajectories of circulating viruses and improve strain selection for seasonal influenza vaccines; development of tools to assess selection pressure and viral fitness; and development of models and platforms for predicting future dominant seasonal virus strains.