Modelling the Victorian Roadmap

Burnet Institute

19 September, 2021

Victoria’s new Roadmap to Deliver the National Plan was released 19 September by the Premier, Dan Andrews.

The Roadmap has been developed based on expert modelling from the Burnet Institute and is set against COVID-19 thresholds including hospitalisation rates, and the vaccination targets already set out in the National Plan to transition Australia’s National COVID-19 Response.

“The modelling helped the Victorian public health teams get a picture of what our hospitalisation rates could look like while cases are still rising and develop trigger points to indicate if the system is becoming overstretched – allowing time to implement further health measures and protect it from becoming overwhelmed,” Premier Andrews said.

“The Burnet modelling also shows that the key to opening up and reducing risk in Victoria will be making sure workers across the state are vaccinated.”

Modelling the Victorian roadmap

Since July 2021 Melbourne has experienced a resurgence in delta variant COVID-19 cases. Despite a lockdown being introduced on 5 August, cases continue to grow, and at 17 September daily diagnoses have reached a 7-day average of 454.

With Victoria’s COVID-19 strategy shifting away from COVID-zero, protecting the health of the population will require achieving high vaccination coverage as quickly as possible, maintaining control of the epidemic to protect the vulnerable, and ensuring that the health system has capacity to provide care to all who need it. An important question is: as vaccine coverage increases, how best can restrictions be eased that prevents health system capacity from being exceeded?

The COVASIM model was used to simulate options for easing of restrictions over the October-December period. Model inputs included data on demographics, contact networks, workforce composition, contact tracing systems and age-specific vaccination rates. As well as options for easing restrictions, additional policies around vaccine allocation and testing were examined to determine potential approaches to further reduce the epidemic peak.

Scenarios were run to estimate the number of COVID-19 infections, hospitalisations and ICU requirements in Melbourne:

  • Maintained lockdown: A counterfactual scenario to set baseline estimates from which restrictions are eased.

  • Roadmap: School and childcare returns throughout October; increased outdoor activities at 70% two-dose vaccine coverage (people 16+ years); retail and indoor activities with density limits commence at 80% adult vaccine coverage; and mandatory vaccination of authorised workers, teachers, childcare workers, parents of children in childcare, hospitality workers, hospitality patrons.

  • Roadmap with additional testing: The roadmap scenario but assuming vaccinated people continue to seek symptomatic testing at the same rate as non-vaccinated people, even for mild symptoms.

  • Roadmap with a 15% reduction in non-household transmission. The roadmap scenario, but with an assumption that a 15% reduction in non-household transmission could be achieved immediately and sustained.

Key findings

  • Even without any easing of restrictions, there is a moderate risk of exceeding health system capacity
  • Based on the current epidemic growth rate, a peak in 7-day average daily diagnoses of 1400-2900 is estimated to occur between 19-31 October
  • Corresponding peaks in hospital and ICU demand were 1200-2500 and 260-550 respectively, with 24% of simulations resulting in hospital demand exceeding 2500 beds.

  • In the roadmap scenario, the significant easing of restrictions at 80% vaccine coverage led to 63% of simulations exceeding 2500 hospital demand, and resulted in a second epidemic peak over mid-December

  • High rates of symptomatic testing among people who are vaccinated could reduce the impact on the health system In a scenario with vaccinated people testing at the same rate as unvaccinated people, the risk of >2500 hospital demand was reduced from 63% to 29%. However, this may be difficult to achieve in practice.

  • If a 15% reduction in non-household risk could be achieved and sustained through a variety of additional targeted public health and testing interventions, the risk of >2500 hospital demand could be reduced to 18%

  • When 80% adult vaccine coverage is reached, the case numbers, hospital and ICU numbers can provide a guide as to the likelihood of the health system capacity being exceeded and whether restrictions can be safely eased consistent with the roadmap or whether a more staggered approach may be required.

  • Due to uncertainty about whether the epidemic growth rate will be sustained, seasonal impacts and vaccine efficacy parameters against the delta strain, updated projections are required as more data becomes available
    Decisions to ease restrictions should be based on the latest epidemiological and health system information.

Image: Figure 4: Roadmap scenario. Includes schools returning to in person learning throughout October; childcare returning and mobility restrictions easing in October; limited outdoor gatherings at 70% two-dose vaccine coverage among people 16+ years; indoor gathering with density limits at 80% two-dose coverage among people 16+ years (Table 2 and Table 3); and mandatory vaccine requirements. Dashed vertical lines represent estimated dates of reaching 70% and 80% two-dose coverage among people 16+ years.

Model assumptions

Models make simplifying assumptions to approximate the real world, particularly where data are not available. Some of these assumptions may lead to the model projections being optimistic or pessimistic compared to what may actually occur. For example, compliance with vaccine mandates in Australian settings is as yet unknown; in the roadmap scenario 95% compliance has been assumed, but the roadmap may be slightly optimistic depending on how successfully it can be implemented. To best interpret the model outputs, it is useful to understand some of the main assumptions that may make these projections optimistic or pessimistic.

Optimistic assumptions

The results could be optimistic (meaning the real world will be worse than estimated) because we have assumed:

  • Schools and childcare can achieve a 50% reduction in transmission risk through ventilation and other mechanisms
  • No waning of vaccine immunity over time
  • No quarantine or testing exemptions have been included for vaccinated people (i.e. vaccinated people continue to be required to quarantine for 14 days if they are identified as contacts)
  • Compliance does not further reduce over time (33% of people are assumed to have had between household contacts in the current lockdown / model calibration period)
  • 95% compliance with vaccine mandates
  • Schools and childcare are able to conduct their own contact tracing
  • Vaccines are delivered equally across all sub-population groups. It is possible that people who are more concerned about COVID-19 and are minimising their number of contacts to lower their COVID-19 risk may be getting vaccinated before people who and less concerned about COVID-19 and are at higher risk.

Pessimistic assumptions

Conversely, the results could be pessimistic (meaning the real world will be better than estimated) because we have assumed:

  • No impact of seasonality, when it is possible that warmer weather may reduce transmission (but unquantified at the moment).
  • The current epidemic growth rate will continue (with the exception of declines due to vaccine immunity), when it is possibly biased by recent infections being concentrated in communities with below average vaccine coverage.

Uncertain assumptions

In addition, the results could be either optimistic OR pessimistic because:

  • Average duration of stay in hospital and ICU is unknown. If it were longer or shorter than we have estimated (e.g. average 11 days in ICU, see appendix) then this would increase or decrease peak demand.
  • Vaccine efficacy assumptions may be better or worse than the parameters we are using (Table 1), but are based on best estimates at the time of analysis.


The findings presented are derived from an individual-based model, which is an imperfect representation of the real world.

  • Results are based on model inputs up to 17 September 2021. As the outbreak evolves and more data becomes available, the uncertainty reduces and it becomes clearer which trajectory we are on.
  • There is uncertainty in the average length of stay in hospital and ICU, and this would impact estimates of peak hospital and ICU demand.
  • Results do not include seasonal effects, which are unknown.
  • Results do not include reduced compliance with restrictions over time. In particular, towards the end of our projections, we have assumed that testing, contact tracing and quarantine continues despite high vaccination coverage, which may overestimate the effectiveness of this system if people are less compliant with QR sign in and other
  • This model currently only attributes basic properties to individuals, specifically age, household structure and participation in different contact networks. The model does not account for any other demographic and health characteristics such as socioeconomic status, comorbidities (e.g. non-communicable diseases) and risk factors (e.g. smoking) and so cannot account for differences in transmission risks, testing, quarantine adherence or disease outcomes for different population subgroups.
  • The model does not include a geospatial component and so cannot capture geographic clustering of vaccination or infection within some communities.
  • The model simulates symptomatic testing by having a parameter for the per day probability of being tested if symptoms are present. This means that the distribution of time from symptom development to testing is binomial, which may differ from the true distribution of time from symptom onset to testing.

Model parameters are based on best-available data at the time of writing. Results from new studies could change estimates of social mixing, contact networks, adherence to policies, quarantine advice, and disease characteristics (e.g. asymptomatic cases), and these could change these results.

Download the Burnet Institute VIC Roadmap Modelling

Contact Details

For more information in relation to this news article, please contact:

Burnet Institute

[email protected]




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