Journal article

Modelling transmission and control of the COVID-19 pandemic in Australia

Sheryl L Chang, Nathan Harding, Cameron Zachreson, Oliver M Cliff, Mikhail Prokopenko



There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing w..

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Awarded by Australian Research Council

Funding Acknowledgements

We are grateful to Stuart Kauffman, Edward Holmes, Joel C. Miller, Paul Ormerod, Kristopher Fair, Philippa Pattison, Mahendra Piraveenan, Manoj Gambhir, Joseph Lizier, Peter Wang, John Parslow, Jonathan Nolan, Neil Davey, Vitali Sintchenko, Tania Sorrell, Ben Marais, and Stephen Leeder, for discussions of various intricacies involved in agentbased modelling of infectious diseases, and computational epidemiology in general. We were supported through the Australian Research Council grants DP160102742 (S.L.C., N.H., O.M.C., C.Z., M.P.) and DP200103005 (M.P.). ACEMod is registered under The University of Sydney's invention disclosure CDIP Ref. 2019-123. AMTraC-19 is registered under The University of Sydney's invention disclosure CDIP Ref. 2020-018. We are thankful for a support provided by High-Performance Computing (HPC) service (Artemis) at the University of Sydney.