SHINE Case Study: COVID-19 Policy Options

Project Details

SHINE has made a leading contribution to COVID-19 policy making.

In collaboration with colleagues at the University of Melbourne’s Transport Health and Urban Design Research Lab (THUD) , we developed an agent-based model for quantifying the impact of policy options like elimination , suppression and mask wearing. This model was successfully used to underpin the Victorian Government’s Roadmap out of lockdown in 2020.

SHINE then linked the agent-based model (as a ‘special’ example of an intervention model) to our PMSLT model to estimate the health gains and costs (including GDP costs) of various policy response options, including:

  • for elimination and suppression strategies in Victoria in 2020 as published in JAMA Health Forum
  • for varying speeds of vaccine program roll out in Pakistan and Armenia for the Asia Development Bank.

COVID-19 Pandemic Tradeoffs is the ongoing platform of modelling.  100s of scenarios are modelled of how infection rates, hospitalisation and death rate, and – critically – time in lockdown vary by policy scenarios (vaccination coverage, border policies impacting the number of vaccinated but infected arrivals per day, and in-country suppression policies). The latest iteration of this modelling is summarized in the 2022 Will Be Better report.

We initiated the Australian COVID-19 Modelling Initiative that showcases outputs from many COVID-19 modelling groups in Australia.

As 2022 approaches, modelling of COVID-19 policy options continues to be important. Our Pandemic Tradeoffs modelling is ideally suited to examining:

  • optimal booster programs
  • optimal border policies
  • optimal use of innovations such as mass rapid antigen testing.

We are ‘open for business’ for clients who want modelling of scenarios; contact us at population-interventions@unimelb.edu.au with queries.

Research Outcomes

Research Publications




Key Contact

For further information about this research, please contact the research group leader.

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