Multiple models helped achieve the successful pandemic management strategies implemented in Australia at both state and national level, yesterday’s first Cosmos Briefing was told.
Professor Jodie McVernon, from Doherty Epidemiology, and Professor Tony Blakely, from the Melbourne School of Population and Global Health, provided a comprehensive overview of their work and the broader application of epidemiological modelling during COVID-19.
The session was hosted by the Lead Scientist of the Royal Institution of Australia, Professor Alan Duffy.
Models can be used to reveal historical trends, simulate futures and assess situations to forecast an outcome. They can thus be geared towards management or elimination of disease, as well as assessing strategies to mitigate risk.
For McVernon, Australian COVID-19 forecasts were adapted from influenza models and updated thanks to information sharing. These strategies were to manage infectious disease and relied heavily on constant communication and updates with close consideration of the capacity of Australia’s health system.
This was incredibly influential to the policies implemented in those first four months, especially because of how little data was available.
Blakely’s experience was completely different, as his previous work was with non-transmissible disease, and the models were geared towards elimination as opposed to management. That coincidentally became relevant in Victoria.
“It just so happened that this model was fit for purpose at working how to come off a wave and achieve elimination, because that’s one of our interests as academics,” he said. “So we became more relevant to policymakers as the pandemic moved on from a very different starting point of Jodie.
“I think that’s quite lovely that you’ve got two different starting points here, that both become useful in different ways. The beauty of living many flowers rise in different ways.”
These models are constantly being updated with new information, including location data. Models based on tracking human movement reveal subtle trends in behaviour, population density and socioeconomic background that help predict what the best course of action is.
“These really detailed analyses of human behaviour and correlation with trends of infection are helping us to think in a much more granular way about the key social and behavioural interventions and public health measures that we can introduce, and the way we can put risk mitigations in place to reduce the spread of infection without just locking people in the houses,” McVernon said.
“That’s our piece of work for the next phase, really.”
Pandemic response and health are not the only areas that rely on these models.
“Climate modelling, I think, is well ahead in its capacity to predict that infectious disease modelling at this point in time,” McVernon added.
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