Australian COVID-19 Data Visualisation and Forecast Modelling Performance Analysis

Abstract

The rise of the Coronavirus pandemic was unanticipated, and it turned into a very serious and catastrophically dangerous scenario especially in terms of financial balance, physical and mental health, population growth, socialization, and globalization. This paper considers Australian COVID-19 data from its beginning on the 25^th of January to this date for experimental study. The popular Microsoft Power BI tool and Python coding language were primarily utilized to visualize the data sets and understand the depth of the COVID-19 situation in Australia. More specifically Python is primarily used in this study on the data to generate visualizations and forecasted models for effective interpretation of the ongoing medical peril. The plots and graphs created significantly extract trends for the accumulative infection rates ongoing in Australia from February 2020 to September 2021. Such important comprehensions of the numerical data set allowed for a graphical understanding and representation with data science applications. Statistical forecasting models such as the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) model were applied to the time series data of Australian COVID-19 infection numbers to predict the future trends of COVID-19 cases in Australia. Finally, we feel this research can help the policymakers and health practitioners to manage such global medical issues more efficiently in the future with the help of data science technology and applications which is the uprising heart of our technological era.