Learning from prepandemic data to forecast viral escape

Abstract Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic—experimental approaches require host polyclonal antibodies to test against 1–16 , and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern 17–19 . To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the…