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

TL;DR

This study uses machine learning to analyze spike protein mutations in SARS-CoV-2, enabling unbiased detection of emerging variants, predicting pandemic waves, and serving as an early warning system for future outbreaks.

Contribution

It introduces a novel ML approach that identifies and tracks virus variants without prior knowledge, validated against genome-based methods, and predicts pandemic waves driven by new variants.

Findings

Each COVID-19 wave is driven by a new emerging variant.

The ML method detects variants when they are only 1% of sequences.

The approach acts as an early warning system for new variants.

Abstract

Applying a ML approach to the temporal variability of the Spike protein sequence enables us to identify, classify and track emerging virus variants. Our analysis is unbiased, in the sense that it does not require any prior knowledge of the variant characteristics, and our results are validated by other informed methods that define variants based on the complete genome. Furthermore, correlating persistent variants of our approach to epidemiological data, we discover that each new wave of the COVID-19 pandemic is driven and dominated by a new emerging variant. Our results are therefore indispensable for further studies on the evolution of SARS-CoV-2 and the prediction of evolutionary patterns that determine current and future mutations of the Spike proteins, as well as their diversification and persistence during the viral spread. Moreover, our ML algorithm works as an efficient early warning system for the emergence of new persistent variants that may pose a threat of triggering a new wave of COVID-19. Capable of a timely identification of potential new epidemiological threats when the variant only represents 1% of the new sequences, our ML strategy is a crucial tool for decision makers to define short and long term strategies to curb future outbreaks. The same methodology can be applied to other viral diseases, influenza included, if sufficient sequencing data is available.