As multiple states grapple with lockdowns and rising COVID-19 cases, Prime Minister Scott Morrison has suggested that Australia can start “coming out of the cave” once vaccination coverage reaches 70% or 80% of the adult population.
“Cases will not be the issue once we get above 70%,” said Morrison this week. “Dealing with serious illness, hospitalisation, ICU capabilities, our ability to respond in those circumstances, that will be our goal. And we will live with this virus as we live with other infectious diseases. That’s what the national plan is all about.”
A four-phase roadmap “out of COVID”, presented by Morrison last month, uses modelling from the Doherty Institute and estimates outcomes based on how many people are fully vaccinated: that is, they have received two doses of AstraZeneca, Pfizer or Moderna vaccines.
“There is light at the end of the tunnel – once we achieve 70%–80% vaccination we will see less transmission of COVID-19 and fewer people with severe illness, and therefore fewer hospitalisations and deaths. COVID-19 won’t go away but it will be easier to control in the future,” says a statement by the Doherty Institute on their modelling.
However, these percentage figures do not represent the entire population of Australia – only those deemed “eligible” for vaccination. Which means that the numbers needed to reach a “reopening point” can appear confusing.
What does 80% vaccination coverage mean?
According to the Doherty Institute, the figure of 80% refers to eligible adult Australians – that is, when 80% of people over the age of 16 have received two doses of a COVID-19 vaccines.
However, 80% of eligible Australians actually only represents 65% of the entire population of Australia – that’s around 16 million people out of a population of 26 million.
Why 80%?
This 80% figure is cited as an estimate of how many people need to be fully vaccinated in order for SARS-CoV-2 to be transmitted at a manageable rate. It’s considered to be when the amount of people admitted to hospitals and ICUs is manageable for the healthcare system.
The model works under the assumption that the Delta variant is the dominant strain and that management strategies continue to include testing, tracing, isolation and quarantine.
Based on the Doherty modelling, when 70% of the adult population (56% of the total population of Australia, or 14 million people) is vaccinated, 1,299 unvaccinated people and 685 vaccinated people could die in the first 180 days, with most above 40 years of age.
However, it states when vaccination rates are at 80% of the adult population, this would be 840 unvaccinated people and 439 vaccinated people, which may be manageable for the healthcare system.
The flat numbers may not seem markedly different, but they are vastly different when comparing them to demographics. Assuming that 16 million people are fully vaccinated at 80% coverage, and 8 million are unvaccinated (as the analysis considered the entire population of Australia), deaths would account for the following:
Based on the modelling, estimated transmission could be reduced by 81%–86%; this could achieve the strategy of “flattening the curve” to a point where health services can sufficiently care for people in critical condition.
But even at 80% vaccination coverage, it is highly likely that there will still be some restrictions that need to be kept in place.
| Deaths per million at: | Vaccinated | Unvaccinated |
|---|---|---|
| 70% vaccine coverage | 48.9 | 162.3 |
| 80% vaccine coverage | 43.6 | 105 |
How good is the Doherty model?
Modelling works by testing various scenarios to predict outcomes – however, this is obviously contingent on the assumptions made about the scenarios. This is the reason the Doherty model tested two major assumptions: vaccination rates among adults, and different levels of vaccine management and restrictions, and how that could affect the burden on healthcare.
“The basic principle of modelling is that any prediction made is only as good as the assumption and data that are fed into the model,” says Ivo Mueller, co-division head of the Population Health and Immunity Division at The Walter and Eliza Hall Institute, Melbourne.
“If unrealistic assumptions are made, vastly unrealistic predictions will follow. This is why, when reviewing model results, experts will always first look at the model’s assumption and algorithm before looking at the model output.
“As the Doherty modelling convincingly shows, vaccination levels and other public health measures reduce the transmission potential of the virus, making it possible to constrain viral transmission with fewer – but, very importantly, not without any – public health measures in place.”
These assumptions do need to be considered with caution, however, because if there is variation in behaviour away from the assumptions made by the model – that is, if management strategies change – there will be a different outcome to what is predicted.
Watch: Cosmos Briefing: Epidemiological Modelling
“The Doherty modelling is very reliant on the assumed effective reproduction number, the relative effects of the virus on children – borrowed from the values for the original Wuhan strain – and the impact of public health measures,” says Emma McBryde, professor at the Australian Institute of Tropical Health and Medicine (AITHM) at James Cook University.
“Models are mostly saying similar things but interpretation is the key difference. My group has shown that the models are very sensitive to assumptions.”
McBryde says that assumptions from the Doherty model include:
- The Delta variant has the same child infection sparing properties that the original strain had
- We can keep the effective reproduction number to 3.6
- We can apply public health measures evenly across all age groups and populations.
“If any of these assumptions change by about 10%, then children are likely to drive COVID-19 infections and we will see quite a different epidemic to that predicted by the Doherty [modelling],” says McBryde.
“In this case, there will be many more children infected, and the infection will spread faster and cause more overall infections.”
“For most, mathematical modelling feels like a black box, not easily subjected to scrutiny,” says Robert Booy, an infectious disease expert at the University of Adelaide. “The Doherty Institute has just released the outcome of complex up-to-date modelling.
“The value of modelling depends on multiple scientific disciplines from epidemiology and psychology to mathematics, history and ethics. It is not an easy task.”
What about other models?
Another report, produced by the Grattan Institute, suggests that “fully vaccinating 80% of all Australians, and 95% of the over-70s, will give us the best chance of gradually returning to normal life – with open borders and no lockdowns”.
The report states: “At 80%, COVID would be in the community, but severe cases would be rare.”
However, this model is based on 80% of the whole population being vaccinated, not a percentage of the eligible population that appears to be Morrison’s target. About 20 million people would need to be fully vaccinated to meet this whole-population 80% target.
“From our own modelling (unpublished), I would concur that Australia would need at least 80% of the whole population vaccinated, and may still need some restrictions such as masks,” says Raina MacIntyre, head of the Biosecurity Program at the Kirby Institute at the University of NSW.
“We can learn from the UK, the US and Israel, which all lifted restrictions between May and June 2021 with vaccination rates around 60% of the whole population. All three countries saw a resurgence of Delta.
“We should take these lessons on board.”