Title: Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations Show Chinese title

Title: 基于机器学习分析刺突蛋白突变的新冠病毒变异驱动多波次模式

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. Show Chinese abstract

Abstract: 应用机器学习方法来分析刺突蛋白序列的时间变化，使我们能够识别、分类和追踪新出现的病毒变异株。 我们的分析是无偏的，也就是说它不需要任何关于变异特征的先验知识，我们的结果通过其他基于完整基因组定义变异的有识方法得到了验证。 此外，将我们方法中持续存在的变异与流行病学数据相关联，我们发现 COVID-19 大流行的每一次新高峰都是由一个新的新兴变异株驱动和主导的。 因此，我们的结果对于进一步研究 SARS-CoV-2 的进化以及预测决定当前和未来刺突蛋白突变及其在病毒传播过程中的多样化和持续性的进化模式至关重要。 此外，我们的机器学习算法可以作为一种高效的早期预警系统，用于检测可能引发 COVID-19 新一轮流行的新型持续性变异株。 当变异仅占新序列的 1% 时，我们的机器学习策略能够及时识别潜在的新流行病威胁，因此是决策者制定短期和长期策略以遏制未来爆发的关键工具。 如果可以获得足够的测序数据，同样的方法也可以应用于其他病毒性疾病，包括流感。