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

TL;DR

This paper introduces a probabilistic model for accurately forecasting COVID-19 dynamics in Australia, including unobserved cases, to aid government decision-making with limited data.

Contribution

It presents a novel partially-observable stochastic process model that predicts actual and unobserved COVID-19 cases with high accuracy using limited data.

Findings

Accurately forecasts COVID-19 cases and unobserved infections.

Provides a tool for evaluating policy scenarios.

Demonstrates low error in predictions.

Abstract

The 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.