Concepts and methods for predicting viral evolution

TL;DR

This paper presents a comprehensive data-driven pipeline that integrates genetic, epidemiological, antigenic, and phenotypic data to predict the evolution and future prevalence of influenza and SARS-CoV-2 viruses, aiding vaccine development.

Contribution

It introduces a novel, integrated predictive analysis method combining multiple data types to forecast viral evolution and inform vaccine strain selection.

Findings

Effective prediction of viral clade frequencies up to one year ahead

Estimation of relative fitness of circulating viral strains

Provision of a publicly accessible prediction platform

Abstract

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.