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Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be...

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Detalles Bibliográficos
Autores principales: Eshragh, Ali, Alizamir, Saed, Howley, Peter, Stojanovski, Elizabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531857/
https://www.ncbi.nlm.nih.gov/pubmed/33007054
http://dx.doi.org/10.1371/journal.pone.0240153
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author Eshragh, Ali
Alizamir, Saed
Howley, Peter
Stojanovski, Elizabeth
author_facet Eshragh, Ali
Alizamir, Saed
Howley, Peter
Stojanovski, Elizabeth
author_sort Eshragh, Ali
collection PubMed
description The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The “partially-observable stochastic process” used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.
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spelling pubmed-75318572020-10-08 Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis Eshragh, Ali Alizamir, Saed Howley, Peter Stojanovski, Elizabeth PLoS One Research Article The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The “partially-observable stochastic process” used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes. Public Library of Science 2020-10-02 /pmc/articles/PMC7531857/ /pubmed/33007054 http://dx.doi.org/10.1371/journal.pone.0240153 Text en © 2020 Eshragh et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Eshragh, Ali
Alizamir, Saed
Howley, Peter
Stojanovski, Elizabeth
Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title_full Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title_fullStr Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title_full_unstemmed Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title_short Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
title_sort modeling the dynamics of the covid-19 population in australia: a probabilistic analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531857/
https://www.ncbi.nlm.nih.gov/pubmed/33007054
http://dx.doi.org/10.1371/journal.pone.0240153
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