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The power and limitations of genomics to track COVID-19 outbreaks: a case study from New Zealand
Jemma L Geoghegan; Jordan Douglas; Xiaoyun Ren; Matt Storey; James Hadfield; Olin K Silander; Nikki E Freed; Lauren Jelley; Sarah Jefferies; Jillian Sherwood; Shevaun Paine; Sue Huang; Andrew Sporle; Michael G Baker; David R Murdoch; Alexei J Drummond; David Welch; Colin R Simpson; Nigel French; Edward C Holmes; Joep de Ligt.
Afiliação
  • Jemma L Geoghegan; University of Otago, Dunedin, New Zealand; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Jordan Douglas; Centre for Computational Evolution, School of Computer Science, University of Auckland, Auckland, New Zealand
  • Xiaoyun Ren; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Matt Storey; Institute of Environmental Science and Research, Wellington, New Zealand.
  • James Hadfield; Fred Hutchinson Cancer Research Centre, Seattle, Washington, USA.
  • Olin K Silander; School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
  • Nikki E Freed; School of Natural and Computational Sciences, Massey University, Auckland, New Zealand.
  • Lauren Jelley; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Sarah Jefferies; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Jillian Sherwood; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Shevaun Paine; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Sue Huang; Institute of Environmental Science and Research, Wellington, New Zealand.
  • Andrew Sporle; Department of Statistics, University of Auckland, New Zealand; iNZight Analytics Ltd., Auckland, New Zealand.
  • Michael G Baker; Department of Public Health, University of Otago, Wellington, New Zealand.
  • David R Murdoch; Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.
  • Alexei J Drummond; Centre for Computational Evolution, School of Computer Science, University of Auckland, Auckland, New Zealand.
  • David Welch; Centre for Computational Evolution, School of Computer Science, University of Auckland, Auckland, New Zealand.
  • Colin R Simpson; School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand.
  • Nigel French; School of Veterinary Science, Massey University, Palmerston North, New Zealand.
  • Edward C Holmes; Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences and School of Medical Sciences, The University of Sydn
  • Joep de Ligt; Institute of Environmental Science and Research, Wellington, New Zealand.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20221853
ABSTRACT
BackgroundReal-time genomic sequencing has played a major role in tracking the global spread and local transmission of SARS-CoV-2, contributing greatly to disease mitigation strategies. After effectively eliminating the virus, New Zealand experienced a second outbreak of SARS-CoV-2 in August 2020. During this August outbreak, New Zealand utilised genomic sequencing in a primary role to support its track and trace efforts for the first time, leading to a second successful elimination of the virus. MethodsWe generated the genomes of 80% of the laboratory-confirmed samples of SARS-CoV-2 from New Zealands August 2020 outbreak and compared these genomes to the available global genomic data. FindingsGenomic sequencing was able to rapidly identify that the new COVID-19 cases in New Zealand belonged to a single cluster and hence resulted from a single introduction. However, successful identification of the origin of this outbreak was impeded by substantial biases and gaps in global sequencing data. InterpretationAccess to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks. FundingThis work was funded by the Ministry of Health of New Zealand, New Zealand Ministry of Business, Innovation and Employment COVID-19 Innovation Acceleration Fund (CIAF-0470), ESR Strategic Innovation Fund and the New Zealand Health Research Council (20/1018 and 20/1041).
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Relato de casos Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Relato de casos Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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