Timely vaccine strain selection and genomic surveillance improve evolutionary forecast accuracy of seasonal influenza A/H3N2.

ABSTRACT: Evolutionary forecasting models inform seasonal influenza vaccine design by predicting which current genetic variants will dominate in the influenza season 12 months later. Forecasting models depend on hemagglutinin sequences from global public health networks to identify current genetic variants (clades) and estimate clade fitnesses. The lag between collection of a clinical sample and public availability of its sequence averages ∼3 months, complicating the 12-month forecasting problem by reducing our understanding of current clade frequencies. Despite continued methodological improvements to forecasting models, these constraints of a 12-month forecast horizon and 3-month submission lags impose an upper bound on any model's accuracy. The SARS-CoV-2 pandemic revealed that modern vaccine tec

SUBMITTER: Huddleston J 

PROVIDER: S-EPMC12677901 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature