Modeling the Dynamics of the COVID-19 Population in Australia: A Probabilistic Analysis

Abstract

AbstractThe novel Corona Virus 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.HighlightsThis work applies a novel and effective approach using a partially-observable stochastic process to study the dynamics of the COVID-19 population in Australia over the 1 March-22 May 2020 period.The key contributions of this work include (but are not limited to):identifying two structural break points in the numbers of new cases coinciding with where the dynamics of the COVID-19 population are altered: the first, a major break point, on 27 March 2020, is one week after implementing the “lockdown restrictions”, and the second minor point on 18 April 2020, is one week after the “Easter break”;forecasting the future daily numbers of new cases up to 28 days in advance with extremely low mean absolute percentage errors (MAPEs) using a relative paucity of data, namely, MAPE of 1.53% using 20 days of data to predict the number of new cases for the following 6 days, MAPE of 0.43% using 34 days of data to predict the number of new cases for the following 14 days, and MAPE of 0.20% using 55 days of data to predict the number of new cases for the following 28 days;estimating approximately 33% of COVID-19 cases as unobserved by 26 March 2020, reducing to less than 5% after implementing the Government’s constructive restrictions;predicting that the growth rate, prior to the Government’s implementation of restrictions, was on a trajectory to infect numbers equal to Australia’s entire population by 24 April 2020;estimating the dynamics of the growth rate of the COVID-19 population to slow down to a rate of 0.820 after the first break point, with a slight rise to 0.979 after the second break point;Advocating the outlined stochastic model as practically beneficial for policy makers when considering implementation and easing of virus restrictions due to the demonstrated sensitivity of the dynamics of the COVID-19 population in Australia to both major and minor system changes.The model developed in this work may further assist policy makers to consider the impact of several potential scenarios in their decision-making processes.