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Understanding how Victoria, Australia gained control of its second COVID-19 wave
During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560916/ https://www.ncbi.nlm.nih.gov/pubmed/34725323 http://dx.doi.org/10.1038/s41467-021-26558-4 |
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author | Trauer, James M. Lydeamore, Michael J. Dalton, Gregory W. Pilcher, David Meehan, Michael T. McBryde, Emma S. Cheng, Allen C. Sutton, Brett Ragonnet, Romain |
author_facet | Trauer, James M. Lydeamore, Michael J. Dalton, Gregory W. Pilcher, David Meehan, Michael T. McBryde, Emma S. Cheng, Allen C. Sutton, Brett Ragonnet, Romain |
author_sort | Trauer, James M. |
collection | PubMed |
description | During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important. |
format | Online Article Text |
id | pubmed-8560916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85609162021-11-15 Understanding how Victoria, Australia gained control of its second COVID-19 wave Trauer, James M. Lydeamore, Michael J. Dalton, Gregory W. Pilcher, David Meehan, Michael T. McBryde, Emma S. Cheng, Allen C. Sutton, Brett Ragonnet, Romain Nat Commun Article During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria’s second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2−56.4%) and of face coverings of 45.9% (95%CrI 32.9−55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important. Nature Publishing Group UK 2021-11-01 /pmc/articles/PMC8560916/ /pubmed/34725323 http://dx.doi.org/10.1038/s41467-021-26558-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Trauer, James M. Lydeamore, Michael J. Dalton, Gregory W. Pilcher, David Meehan, Michael T. McBryde, Emma S. Cheng, Allen C. Sutton, Brett Ragonnet, Romain Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title | Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title_full | Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title_fullStr | Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title_full_unstemmed | Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title_short | Understanding how Victoria, Australia gained control of its second COVID-19 wave |
title_sort | understanding how victoria, australia gained control of its second covid-19 wave |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560916/ https://www.ncbi.nlm.nih.gov/pubmed/34725323 http://dx.doi.org/10.1038/s41467-021-26558-4 |
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