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Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations.
Adele de Hoffer,Shahram Vatani,Corentin Cot,Giacomo Cacciapaglia,Maria Luisa Chiusano,Andrea Cimarelli,Francesco Conventi,Antonio Giannini,Stefan Hohenegger,Francesco Sannino +9 more
TLDR
In this paper, the temporal variability of the Spike protein sequence has been used to identify, classify and track emerging virus variants, which can be used as an early warning system for the emergence of new persistent variants that may pose a threat of triggering a new wave of COVID-19.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.read more
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Binary codes capable of correcting deletions, insertions, and reversals
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Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus.
Bette T. Korber,Will Fischer,Sandrasegaram Gnanakaran,Hyejin Yoon,James Theiler,Werner Abfalterer,Nick Hengartner,Elena E. Giorgi,Tanmoy Bhattacharya,Brian T. Foley,Kathryn M. Hastie,Matthew Parker,David G Partridge,Cariad Evans,Timothy M. Freeman,Thushan I de Silva,Adrienne Angyal,Rebecca Brown,Laura Carrilero,Luke R. Green,Luke R. Green,Luke R. Green,Danielle C. Groves,Katie Johnson,Alexander J Keeley,Benjamin B Lindsey,Paul J. Parsons,Mohammad Raza,Sarah Rowland-Jones,Nikki Smith,Rachel Tucker,Dennis Wang,Matthew Wyles,Charlene McDanal,Lautaro G. Perez,Haili Tang,Alex Moon-Walker,Alex Moon-Walker,Alex Moon-Walker,Sean P. J. Whelan,Celia C. LaBranche,Erica Ollmann Saphire,David C. Montefiori +42 more
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GISAID: Global initiative on sharing all influenza data - from vision to reality.
Yuelong Shu,John W. McCauley +1 more
TL;DR: This poster presents a poster presenting a poster presented at the 2016 International Conference of the Association for the Study of Viral Influenza and its Disruption in China, where it was presented for the first time.
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A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology.
Andrew Rambaut,Edward C. Holmes,Áine O'Toole,Verity Hill,John T. McCrone,Christopher Ruis,Louis du Plessis,Oliver G. Pybus +7 more
TL;DR: A rational and dynamic virus nomenclature that uses a phylogenetic framework to identify those lineages that contribute most to active spread and is designed to provide a real-time bird’s-eye view of the diversity of the hundreds of thousands of genome sequences collected worldwide.