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Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand

During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, R(t), during Aotearoa New Zealand’s August 2021 outbreak of the Delta variant o...

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Autores principales: Binny, Rachelle N., Lustig, Audrey, Hendy, Shaun C., Maclaren, Oliver J., Ridings, Kannan M., Vattiato, Giorgia, Plank, Michael J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583856/
https://www.ncbi.nlm.nih.gov/pubmed/36275456
http://dx.doi.org/10.7717/peerj.14119
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author Binny, Rachelle N.
Lustig, Audrey
Hendy, Shaun C.
Maclaren, Oliver J.
Ridings, Kannan M.
Vattiato, Giorgia
Plank, Michael J.
author_facet Binny, Rachelle N.
Lustig, Audrey
Hendy, Shaun C.
Maclaren, Oliver J.
Ridings, Kannan M.
Vattiato, Giorgia
Plank, Michael J.
author_sort Binny, Rachelle N.
collection PubMed
description During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, R(t), during Aotearoa New Zealand’s August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading drivers of variation in transmission in this period and we describe how changes in these factors coincided with changes in R(t). Alert Level 4, New Zealand’s most stringent restriction setting which includes stay-at-home measures, was initially effective at reducing the median R(t)to 0.6 (90% CrI 0.4, 0.8) on 29 August 2021. As New Zealand eased certain restrictions and switched from an elimination strategy to a suppression strategy, R(t) subsequently increased to a median 1.3 (1.2, 1.4). Increasing vaccination coverage along with regional restrictions were eventually sufficient to reduce R(t)below 1. The outbreak peaked at an estimated 198 (172, 229) new infected cases on 10 November, after which cases declined until January 2022. We continue to update R(t)estimates in real time as new case data become available to inform New Zealand’s ongoing pandemic response.
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spelling pubmed-95838562022-10-21 Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand Binny, Rachelle N. Lustig, Audrey Hendy, Shaun C. Maclaren, Oliver J. Ridings, Kannan M. Vattiato, Giorgia Plank, Michael J. PeerJ Microbiology During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, R(t), during Aotearoa New Zealand’s August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading drivers of variation in transmission in this period and we describe how changes in these factors coincided with changes in R(t). Alert Level 4, New Zealand’s most stringent restriction setting which includes stay-at-home measures, was initially effective at reducing the median R(t)to 0.6 (90% CrI 0.4, 0.8) on 29 August 2021. As New Zealand eased certain restrictions and switched from an elimination strategy to a suppression strategy, R(t) subsequently increased to a median 1.3 (1.2, 1.4). Increasing vaccination coverage along with regional restrictions were eventually sufficient to reduce R(t)below 1. The outbreak peaked at an estimated 198 (172, 229) new infected cases on 10 November, after which cases declined until January 2022. We continue to update R(t)estimates in real time as new case data become available to inform New Zealand’s ongoing pandemic response. PeerJ Inc. 2022-10-17 /pmc/articles/PMC9583856/ /pubmed/36275456 http://dx.doi.org/10.7717/peerj.14119 Text en ©2022 Binny et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Microbiology
Binny, Rachelle N.
Lustig, Audrey
Hendy, Shaun C.
Maclaren, Oliver J.
Ridings, Kannan M.
Vattiato, Giorgia
Plank, Michael J.
Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title_full Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title_fullStr Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title_full_unstemmed Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title_short Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand
title_sort real-time estimation of the effective reproduction number of sars-cov-2 in aotearoa new zealand
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583856/
https://www.ncbi.nlm.nih.gov/pubmed/36275456
http://dx.doi.org/10.7717/peerj.14119
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