Fig. 5: Identifying strains with high escape potential and forecasting escape for future pandemics.
From: Learning from prepandemic data to forecast viral escape
a, Prepandemic EVEscape scores computed for pandemic strains correlate with fold reduction in pseudovirus 50% neutralization titre19 for each strain relative to the Wuhan strain (ρ = 0.81, n = 21). Linear regression line shown with a 95% confidence interval. b, Distributions of newly emerging EVEscape strain (unique combination of mutations) scores for non-VOCs throughout 15 periods of the pandemic, with counts of unique new strains per period. EVEscape strain scores increased throughout the pandemic. High-frequency VOC (occurring more than 5,000 times) scores are shown as vertical lines in the first period in which each emerged; new VOCs were predicted to have higher escape scores than most strains in all previous time periods. c, Pandemic circulating strains are grouped according to their EVEscape decile relative to other strains emerging in the same non-overlapping two-week surveillance window. The relative prevalence of each EVEscape decile over the course of the pandemic is plotted in a stacked line-plot. More than 40% of circulating strains on average fall into the top 10% bin. Proportions do not sum to 100% as strains that emerged before the surveillance period of September 2020 to June 2023 are not included. d, VOCs (dotted lines) were among the highest scoring of hundreds or thousands of new strains (histograms) within their two-week window of emergence, enabling EVEscape to forecast which strains will dominate as soon as they appear after only a single observation. e, Site-wise maximum EVEscape scores on Lassa virus glycoprotein structure (PDB: 7PUY). We show agreement between sites of high EVEscape scores (in red) and escape mutations with experimental evidence (shown with spheres). freq., frequency.
