Unlocking Insights: Analysing COVID-19 Lockdown Policies and Mobility Data in Victoria, Australia, through a Data-Driven Machine Learning Approach

Shiyang Lyu, Oyelola Adegboye, Kiki Adhinugraha, Theophilus I. Emeto and David Taniar ()
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Shiyang Lyu: Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
Oyelola Adegboye: Menzies School of Health Research, Charles Darwin University, Casuarina, NT 0811, Australia
Kiki Adhinugraha: Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Theophilus I. Emeto: Public Health and Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
David Taniar: Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia

Data, 2023, vol. 9, issue 1, 1-20

Abstract: The state of Victoria, Australia, implemented one of the world’s most prolonged cumulative lockdowns in 2020 and 2021. Although lockdowns have proven effective in managing COVID-19 worldwide, this approach faced challenges in containing the rising infection in Victoria. This study evaluates the effects of short-term (less than 60 days) and long-term (more than 60 days) lockdowns on public mobility and the effectiveness of various social restriction measures within these periods. The aim is to understand the complexities of pandemic management by examining various measures over different lockdown durations, thereby contributing to more effective COVID-19 containment methods. Using restriction policy, community mobility, and COVID-19 data, a machine-learning-based simulation model was proposed, incorporating analysis of correlation, infection doubling time, and effective lockdown date. The model result highlights the significant impact of public event cancellations in preventing COVID-19 infection during short- and long-term lockdowns and the importance of international travel controls in long-term lockdowns. The effectiveness of social restriction was found to decrease significantly with the transition from short to long lockdowns, characterised by increased visits to public places and increased use of public transport, which may be associated with an increase in the effective reproduction number ( R t ) and infected cases.

Keywords: data driven; infection control; epidemiology; healthcare; digital health; social restriction; machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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