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Real-time genomics to track COVID-19 post-elimination border incursions in Aotearoa New Zealand
Jordan Douglas; Jemma L Geoghegan; James Hadfield; Remco Bouckaert; Matthew Storey; Xiaoyun Ren; Joep de Ligt; Nigel French; David Welch.
Afiliación
  • Jordan Douglas; University of Auckland
  • Jemma L Geoghegan; University of Otago
  • James Hadfield; Fred Hutchinson Cancer Research Center
  • Remco Bouckaert; University of Auckland
  • Matthew Storey; Institute of Environmental Science and Research Limited
  • Xiaoyun Ren; Institute of Environmental Science and Research Limited
  • Joep de Ligt; Institute of Environmental Science and Research Limited
  • Nigel French; Massey University
  • David Welch; University of Auckland
Preprint en Inglés | medRxiv | ID: ppmedrxiv-21257194
ABSTRACT
There have been thirteen known COVID-19 community outbreaks in Aotearoa New Zealand since the virus was first eliminated in May 2020, two of which led to stay-at-home orders being issued by health officials. These outbreaks originated at the border; via isolating returnees, airline workers, and cargo vessels. With a public health system informed by real-time viral genomic sequencing which typically had complete genomes within 12 hours after a community-based positive COVID-19 test, every outbreak was well-contained with a total of 225 community cases, resulting in three deaths. Real-time genomics were essential for establishing links between cases when epidemiological data could not, and for identifying when concurrent outbreaks had different origins. By reconstructing the viral transmission history from genomic sequences, here we recount all thirteen community outbreaks and demonstrate how genomics played a vital role in containing them. SummaryThe authors recount the role of real-time viral genomics in containing the COVID-19 community outbreaks of Aotearoa New Zealand.
Licencia
cc_by_nc
Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Estudio pronóstico Idioma: Inglés Año: 2021 Tipo del documento: Preprint
Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Estudio pronóstico Idioma: Inglés Año: 2021 Tipo del documento: Preprint
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