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

Ali Eshragh; Saed Alizamir; Peter Howley; Elizabeth Stojanovski

doi:10.1371/journal.pone.0240153

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

Ali Eshragh^*, Saed Alizamir, Peter Howley, Elizabeth Stojanovski

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

33 Downloads (Pure)

Abstract

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.

Original language	English
Article number	e0240153
Pages (from-to)	1-19
Number of pages	19
Journal	PLoS ONE
Volume	15
Issue number	10
DOIs	https://doi.org/10.1371/journal.pone.0240153
Publication status	Published - 2 Oct 2020
Externally published	Yes

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

A correction exists for this article and can be found in PLoS ONE (2022) Vol 17(8) art. e0272762 at doi: 10.1371/journal.pone.0272762

Access to Document

10.1371/journal.pone.0240153Licence: CC BY

Publisher version (open access)Final published version, 1.86 MBLicence: CC BY

Cite this

@article{77d6799acafa4d17a35821818360082b,

title = "Modeling the dynamics of the COVID-19 population in Australia: a probabilistic analysis",

abstract = "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.",

author = "Ali Eshragh and Saed Alizamir and Peter Howley and Elizabeth Stojanovski",

note = "Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. A correction exists for this article and can be found in PLoS ONE (2022) Vol 17(8) art. e0272762 at doi: 10.1371/journal.pone.0272762",

year = "2020",

month = oct,

day = "2",

doi = "10.1371/journal.pone.0240153",

language = "English",

volume = "15",

pages = "1--19",

journal = "PLoS ONE",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "10",

}

TY - JOUR

T1 - Modeling the dynamics of the COVID-19 population in Australia

T2 - a probabilistic analysis

AU - Eshragh, Ali

AU - Alizamir, Saed

AU - Howley, Peter

AU - Stojanovski, Elizabeth

N1 - Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. A correction exists for this article and can be found in PLoS ONE (2022) Vol 17(8) art. e0272762 at doi: 10.1371/journal.pone.0272762

PY - 2020/10/2

Y1 - 2020/10/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85092271313&partnerID=8YFLogxK

UR - https://doi.org/10.1371/journal.pone.0272762

U2 - 10.1371/journal.pone.0240153

DO - 10.1371/journal.pone.0240153

M3 - Article

C2 - 33007054

AN - SCOPUS:85092271313

SN - 1932-6203

VL - 15

SP - 1

EP - 19

JO - PLoS ONE

JF - PLoS ONE

IS - 10

M1 - e0240153

ER -

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

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

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

Cite this