Data-driven analysis of amino acid change dynamics timely reveals SARS-CoV-2 variant emergence.

Our early-warning system for variant emergence To identify clusters of changes characterized by an increasing trend in their prevalence time-series, as expected in the case of emerging variants, we use the non-parametric Kendall’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau _B$$\end{document} τ B statistic 47 , as implemented in the ktaub MATLAB package 48 , namely to evaluate the ordinal association between change prevalence and sampling time.

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We regarded as warnings of possible variant emergence those clusters characterized by (1) a positive trend in the prevalence time-series of the constituting amino acid changes, and (2) being sufficiently different from clusters that caused previous warnings (to avoid extracting twice a set describing the same candidate variant).

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