The wide availability of viral genomes on public databases has made possible the data-driven study of the evolution of viruses, especially SARS-CoV-2, responsible for the recent COVID-19 pandemic. Such methods leverage on properties of data and available domain knowledge and employ data science methods, such as time-series clustering. A number of tools are also available to explore the variants’ trends and suggest hypotheses on the evolutionary mechanisms of the virus. Several are the directions to further develop the concept of an early warning system for current and future pandemics.
Data-Driven Methods for Viral Variants’ Identification
Bernasconi, Anna
2024-01-01
Abstract
The wide availability of viral genomes on public databases has made possible the data-driven study of the evolution of viruses, especially SARS-CoV-2, responsible for the recent COVID-19 pandemic. Such methods leverage on properties of data and available domain knowledge and employ data science methods, such as time-series clustering. A number of tools are also available to explore the variants’ trends and suggest hypotheses on the evolutionary mechanisms of the virus. Several are the directions to further develop the concept of an early warning system for current and future pandemics.File in questo prodotto:
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