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Applying a ML approach to the temporal variability of the Spike proteinsequence enables us to identify, classify and track emerging virus variants.Our analysis is unbiased, in the sense that it does not require any priorknowledge of the variant characteristics, and our results are validated byother informed methods that define variants based on the complete genome.Furthermore, correlating persistent variants of our approach to epidemiologicaldata, we discover that each new wave of the COVID-19 pandemic is driven anddominated by a new emerging variant. Our results are therefore indispensablefor further studies on the evolution of SARS-CoV-2 and the prediction ofevolutionary patterns that determine current and future mutations of the Spikeproteins, as well as their diversification and persistence during the viralspread. Moreover, our ML algorithm works as an efficient early warning systemfor the emergence of new persistent variants that may pose a threat oftriggering a new wave of COVID-19. Capable of a timely identification ofpotential new epidemiological threats when the variant only represents 1% ofthe new sequences, our ML strategy is a crucial tool for decision makers todefine short and long term strategies to curb future outbreaks. The samemethodology can be applied to other viral diseases, influenza included, ifsufficient sequencing data is available.

Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations