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Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that include...

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Autores principales: Moss, Robert, Price, David J., Golding, Nick, Dawson, Peter, McVernon, Jodie, Hyndman, Rob J., Shearer, Freya M., McCaw, James M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228456/
https://www.ncbi.nlm.nih.gov/pubmed/37253758
http://dx.doi.org/10.1038/s41598-023-35668-6
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author Moss, Robert
Price, David J.
Golding, Nick
Dawson, Peter
McVernon, Jodie
Hyndman, Rob J.
Shearer, Freya M.
McCaw, James M.
author_facet Moss, Robert
Price, David J.
Golding, Nick
Dawson, Peter
McVernon, Jodie
Hyndman, Rob J.
Shearer, Freya M.
McCaw, James M.
author_sort Moss, Robert
collection PubMed
description As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.
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spelling pubmed-102284562023-06-01 Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020 Moss, Robert Price, David J. Golding, Nick Dawson, Peter McVernon, Jodie Hyndman, Rob J. Shearer, Freya M. McCaw, James M. Sci Rep Article As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response. Nature Publishing Group UK 2023-05-30 /pmc/articles/PMC10228456/ /pubmed/37253758 http://dx.doi.org/10.1038/s41598-023-35668-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Moss, Robert
Price, David J.
Golding, Nick
Dawson, Peter
McVernon, Jodie
Hyndman, Rob J.
Shearer, Freya M.
McCaw, James M.
Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title_full Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title_fullStr Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title_full_unstemmed Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title_short Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020
title_sort forecasting covid-19 activity in australia to support pandemic response: may to october 2020
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228456/
https://www.ncbi.nlm.nih.gov/pubmed/37253758
http://dx.doi.org/10.1038/s41598-023-35668-6
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