Put the Safe Delivery App in the hands of midwives in PNG
Put the Safe Delivery App in the hands of midwives in PNG
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.
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.
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.
The results could be optimistic (meaning the real world will be worse than estimated) because we have assumed:
Conversely, the results could be pessimistic (meaning the real world will be better than estimated) because we have assumed:
In addition, the results could be either optimistic OR pessimistic because:
The findings presented are derived from an individual-based model, which is an imperfect representation of the real world.
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.