- Title
- Modeling the dynamics of the COVID-19 population in Australia: a probabilistic analysis
- Creator
- Eshragh, Ali; Alizamir, Saed; Howley, Peter; Stojanovski, Elizabeth
- Relation
- PLoS One Vol. 15, Issue 10, no. e0240153
- Publisher Link
- http://dx.doi.org/10.1371/journal.pone.0240153
- Publisher
- Public Library of Science (PLoS)
- Resource Type
- journal article
- Date
- 2020
- 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.
- Subject
- COVID 19; virus testing; australia Stochastic processes; pandemics Markov processes; population dynamics; medical risk factors
- Identifier
- http://hdl.handle.net/1959.13/1419833
- Identifier
- uon:37503
- Identifier
- ISSN:1932-6203
- Rights
- © 2020 Eshragh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Language
- eng
- Full Text
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