How well can we model the path out of lockdowns?

The path out of major lockdowns in Australia looked a little clearer last week, with the federal government announcing COVID-19 vaccination targets of 70% and 80% among the eligible population as triggers for reduced restrictions.

The Doherty Institute released the modelling it provided to the National Cabinet to help set those targets. The model is based on a single national Delta variant epidemic, and looks at the outcomes for Australia if we had 50%, 60%, 70% and 80% of the population aged 16 and over vaccinated; how many cases, hospitalisations and deaths would occur and how well health departments would be able to test, trace, isolate and quarantine cases.

Population health expert Professor Ivo Mueller, of the Walter and Eliza Hall Institute, said in an AusSMC expert reaction that the modelling provided a framework for how Australia can transition from closed borders and frequent lockdowns to a more open way of life.

“The presence of the highly infectious Delta strain means the current strategy of aggressive suppression with early, short and sharp lockdowns will need to continue until we reach 70% vaccine coverage,” Mueller said.

He said 70% was not a “magical threshold”, with transmission potential continuously decreasing as vaccination coverage increases. This, he said, would lead to more effective and less frequent lockdowns and by the time 70%of the eligible population is vaccinated short lockdowns and low-level public health measures should be enough to keep the virus under control.

“Only once we approach 80% vaccine coverage will life be able to gradually approach a ‘new normal’,” Mueller said.

A key takeaway from the Doherty modelling is that extending eligibility to “key transmitting age groups”, such as younger people, has the potential to reduce transmission further even with less vaccination coverage.

The modelling showed that at this stage of the national COVID-19 vaccine rollout, extending vaccinations to all adults, rather than the oldest first, could lower rates of death and hospitalisations.

However, because Australia’s current supply of the vaccines most people desire is limited, the researchers went on to model how an “all adults” scenario compared with the planned national COVID-19 rollout, under which vaccines would available for 30–39 year olds on 31 August 2021, and 16–29 years olds from 11 October – a strategy the modelling called “transmission reducing”.

This modelling found that a transmission reducing vaccine strategy was marginally better than an “all adults” strategy.

Professor Robert Booy from the University of Sydney said the question of vaccinating the young or the old is a nuanced dilemma.

“The risk of dying from COVID-19 in your 30s is four times higher than for a teenager; the risk of dying from COVID-19 in your 50s is almost 40 times higher than a teenager,” Booy said. “So what to do? The priority remains to get at-risk people vaccinated rapidly.”

However, he said a person in their 30s might be a schoolteacher or an essential worker delivering food and therefore unable to restrict their movement.

“We need to think more outside the box,” he said. “Efforts to dramatically improve supply need to continue so that current demand and need can be met.”

University of Sydney Associate Professor Alexandra Martiniuk said even younger people needed to be considered more when planning a path out of lockdowns. “Using vaccination targets in the over-16 year age group as milestones for moving between phases of the opening-up plan misses an important cohort of the population – children,” she said.

The Doherty modelling did look at expanding the vaccine program to the 12–15 year age group, but if found that it would have minimal impact on transmission and clinical outcomes. Martiniuk argues that we need a scenario modelling vaccination for all ages, including kids under 12.

“We need milestones to take into account children at all times,” she said.

Professor Emma McBryde from the Australian Institute of Tropical Health and Medicine at James Cook University is also concerned about some assumptions in the modelling when it comes to children.

Her own vaccination modelling is based on an assumption that as many as five people could catch the virus from an infected person, a figure called the reproduction number. If that’s the case, McBryde says we need to vaccinate children to achieve herd immunity.

“The Doherty Institute assumes that the reproduction number (they call this transmission potential) is only 3.6,” McBryde said. “If we model this, we get the same findings but the results are highly sensitive to the assumed effective reproduction number.”

Professor Nikolai Petrovsky from Flinders University agreed that the Doherty model was likely to be very sensitive to how strongly vaccines reduced transmission, a question to which we may yet not fully know the answer. He said vaccine effects on transmission are notoriously hard to measure, and in most cases should be considered no better than educated guesses.

“Recent Covid-19 outbreaks have occurred in populations with high vaccination coverage approximating those predicted by the model,” Petrovsky said. “With Delta strain achieving exceptionally high transmission rates even amongst the vaccinated, the model and its assumptions need to be closely examined, before such data is relied upon to make any policy decisions.”

Martiniuk said while we need to be cautious of the simplified nature of modelling, it was a good place to start when planning how to move forward.

“The Doherty Institute Modelling Report for National Cabinet is useful in understanding how we might transition between the various phases of opening up,” she said. “The modelling is useful in that it is based on Delta in terms of transmission, severity and vaccine effectiveness.

“However, we do need to be wary that the model is based on this being a ‘single national epidemic’ in order to simplify the modelling. We also need to be wary of new variants of concern emerging beyond Delta, which of course would affect any current model’s ability to predict future scenarios.”