A Phylodynamic Workflow to Rapidly Gain Insights into the Dispersal History and Dynamics of SARS-CoV-2 Lineages | Zendy

Simon Dellicour | Zendy; Keith Durkin | Zendy; Samuel L. Hong | Zendy; Bert Vanmechelen | Zendy; Joan MartíCarreras | Zendy; Mandev S. Gill | Zendy; Cécile Meex | Zendy; Sébastien Bontems | Zendy; Emmanuel André | Zendy; Marius Gilbert | Zendy; Conor R. Walker | Zendy; Nicola De Maio | Zendy; Nuno R. Faria | Zendy; James Hadfield | Zendy; MariePierre Hayette | Zendy; Vincent Bours | Zendy; Tony Wawina-Bokalanga | Zendy; Maria Artesi | Zendy; Guy Baele | Zendy; Piet Maes | Zendy

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A Phylodynamic Workflow to Rapidly Gain Insights into the Dispersal History and Dynamics of SARS-CoV-2 Lineages

Author(s) -

Simon Dellicour,

Keith Durkin,

Samuel L. Hong,

Bert Vanmechelen,

Joan MartíCarreras,

Mandev S. Gill,

Cécile Meex,

Sébastien Bontems,

Emmanuel André,

Marius Gilbert,

Conor R. Walker,

Nicola De Maio,

Nuno R. Faria,

James Hadfield,

MariePierre Hayette,

Vincent Bours,

Tony Wawina-Bokalanga,

Maria Artesi,

Guy Baele,

Piet Maes

Publication year - 2020

Publication title -

molecular biology and evolution

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.637

H-Index - 218

eISSN - 1537-1719

pISSN - 0737-4038

DOI - 10.1093/molbev/msaa284

Subject(s) - biological dispersal , biology , pandemic , workflow , evolutionary biology , pipeline (software) , data science , covid-19 , viral phylodynamics , transmission (telecommunications) , genome , computational biology , phylogeography , phylogenetics , computer science , genetics , disease , demography , infectious disease (medical specialty) , medicine , telecommunications , population , pathology , database , sociology , gene , programming language

Since the start of the COVID-19 pandemic, an unprecedented number of genomic sequences of SARS-CoV-2 have been generated and shared with the scientific community. The unparalleled volume of available genetic data presents a unique opportunity to gain real-time insights into the virus transmission during the pandemic, but also a daunting computational hurdle if analyzed with gold-standard phylogeographic approaches. To tackle this practical limitation, we here describe and apply a rapid analytical pipeline to analyze the spatiotemporal dispersal history and dynamics of SARS-CoV-2 lineages. As a proof of concept, we focus on the Belgian epidemic, which has had one of the highest spatial densities of available SARS-CoV-2 genomes. Our pipeline has the potential to be quickly applied to other countries or regions, with key benefits in complementing epidemiological analyses in assessing the impact of intervention measures or their progressive easement.

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