  The seasonal human influenza virus undergoes rapid evolution, leading to
significant changes in circulating viral strains from year to year. These
changes are typically driven by adaptive mutations, particularly in the
antigenic epitopes, the regions of the viral surface protein haemagglutinin
targeted by human antibodies. Here we describe a consistent set of methods for
data-driven predictive analysis of viral evolution. Our pipeline integrates
four types of data: (1) sequence data of viral isolates collected on a
worldwide scale, (2) epidemiological data on incidences, (3) antigenic
characterization of circulating viruses, and (4) intrinsic viral phenotypes.
From the combined analysis of these data, we obtain estimates of relative
fitness for circulating strains and predictions of clade frequencies for
periods of up to one year. Furthermore, we obtain comparative estimates of
protection against future viral populations for candidate vaccine strains,
providing a basis for pre-emptive vaccine strain selection. Continuously
updated predictions obtained from the prediction pipeline for influenza and
SARS-CoV-2 are available on the website https://previr.app.
