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

Author

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  • Ali Eshragh
  • Saed Alizamir
  • Peter Howley
  • Elizabeth Stojanovski

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.

Suggested Citation

  • Ali Eshragh & Saed Alizamir & Peter Howley & Elizabeth Stojanovski, 2020. "Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0240153
    DOI: 10.1371/journal.pone.0240153
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    Cited by:

    1. Peter Lloyd & Robert Dixon, 2021. "Modelling the Spread of the Coronavirus: A View from Economics," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 54(1), pages 36-56, March.
    2. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2024. "Testing, Voluntary Social Distancing, and the Spread of an Infection," Operations Research, INFORMS, vol. 72(2), pages 533-548, March.
    3. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.

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