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Early phylodynamics analysis of the COVID-19 epidemics in France
Gonché Danesh; Baptiste Elie; Yannis Michalakis; Mircea T. Sofonea; Antonin Bal; Sylvie Behillil; Grégory Destras; David Boutolleau; Sonia Burrel; Anne-Geneviève Marcelin; Jean-Christophe Plantier; Vincent Thibault; Etienne Simon-Loriere; Sylvie van der Werf; Bruno Lina; Laurence Josset; Vincent Enouf; Samuel Alizon.
Afiliação
  • Gonché Danesh; Université de Montpellier
  • Baptiste Elie; Université de Montpellier
  • Yannis Michalakis; CNRS
  • Mircea T. Sofonea; Univ. Montpellier
  • Antonin Bal; Hospices Civils de Lyon
  • Sylvie Behillil; Institut Pasteur
  • Grégory Destras; Institut des Agents Infectieux - Hospices Civils de Lyon
  • David Boutolleau; APHP, Sorbonne Université
  • Sonia Burrel; APHP, Sorbonne Université
  • Anne-Geneviève Marcelin; APHP, Sorbonne Université
  • Jean-Christophe Plantier; CHU de Rouen
  • Vincent Thibault; CHU de Rennes
  • Etienne Simon-Loriere; Institut Pasteur
  • Sylvie van der Werf; Institut Pasteur
  • Bruno Lina; Université de Lyon
  • Laurence Josset; Université de Lyon
  • Vincent Enouf; Institut Pasteur
  • Samuel Alizon; CNRS
Preprint em En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20119925
ABSTRACT
France was one of the first countries to be reached by the COVID-19 pandemic. Here, we analyse 196 SARS-Cov-2 genomes collected between Jan 24 and Mar 24 2020, and perform a phylodynamics analysis. In particular, we analyse the doubling time, reproduction number ([R]t) and infection duration associated with the epidemic wave that was detected in incidence data starting from Feb 27. Different models suggest a slowing down of the epidemic in Mar, which would be consistent with the implementation of the national lock-down on Mar 17. The inferred distributions for the effective infection duration and[R] t are in line with those estimated from contact tracing data. Finally, based on the available sequence data, we estimate that the French epidemic wave originated between mid-Jan and early Feb. Overall, this analysis shows the potential to use sequence genomic data to inform public health decisions in an epidemic crisis context and calls for further analyses with denser sampling.
Licença
cc_by_nc
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Observational_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Tipo de estudo: Observational_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint