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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7531857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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