Journal article

Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand

Jemma L Geoghegan, Xiaoyun Ren, Matthew Storey, James Hadfield, Lauren Jelley, Sarah Jefferies, Jill Sherwood, Shevaun Paine, Sue Huang, Jordan Douglas, Fabio K Mendes, Andrew Sporle, Michael G Baker, David R Murdoch, Nigel French, Colin R Simpson, David Welch, Alexei J Drummond, Edward C Holmes, Sebastian Duchene Show all

Nature Communications | NATURE RESEARCH | Published : 2020

Abstract

New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nationwide 'lockdown' of all non-essential services to curb the spread of COVID-19. Here, we generate 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected during the 'first wave', representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. These data helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, Re of New Zealand's largest cluster decre..

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Grants

Awarded by New Zealand Ministry of Business, Innovation and Employment COVID-19 Innovation Acceleration Fund


Awarded by New Zealand Health Research Council


Funding Acknowledgements

This 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). We thank the ATRIC network for making their protocols and tools openly available and specifically Josh Quick for sending the initial V1 and V3 amplification primers. We thank Genomics Aotearoa for their support. We thank the diagnostic laboratories that performed the initial RT-PCRs and referred samples for sequencing as well as the public health units for providing epidemiological data. We thank the Nextstrain team for their support and timely global and local analysis. We thank all those who have contributed SARS-CoV-2 sequences to GenBank and GISAID databases.