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Same storm, different boats: a reflection on COVID-19 models

12 November 2020
Modellers reflect on highly cited COVID-19 research
In March, a group of researchers from the Centre for Complex Systems modelled how varying rates of social distancing could affect the spread of the COVID-19 pandemic across a very diverse Australian population.

From left to right, Professor Mikhail Prokopenko, Dr Cameron Zachreson, Sheryl Chang, Nathan Harding and Dr Oliver Cliff from the Centre for Complex Systems.

In March 2020, as the pandemic was sweeping the globe and establishing roots in Australia, the Complex Systems Centre at the University of Sydney modelled how varying rates of social distancing could affect the spread of the COVID-19 pandemic across a very diverse Australian population.

Over the next three months, Australia’s pandemic spread followed a similar pattern to the scenario modelled by Professor Mikhail Prokopenko and his team – which predicted a pandemic peak in April.

Their research became one of the most highly cited and reported COVID-19 models (with more than 160 citations according to Google Scholar on November 11th, 2020).

Eight months on, he and his team reflect on the research, what they learnt, and how they can continue to use modelling to safeguard Australia’s future health security.

The research was released via pre-print in March and was this week published in Nature Communications

Pandemic's early stages required quick thinking 

The team's March modelling identified a pandemic tipping point in Australia, which was the minimal level of effective social distancing (80 percent) required to contain the pandemic spread.

“We had to act quickly, and in March the virus was not yet well understood. Some approximations had to be made in our model, while retaining our focus on actionable interventions," said Professor Mikhail Prokopenko.

“Not only do we now understand the virus better and how it behaves, but we also understand how human movement, social interaction and government policy influence its spread.

“As the fight against COVID-19 continues, we hope that many contentious and rhetoric-rich debates can be resolved by sharp quantitative modelling.”

Calibration key for modelling

The research team's main aim was to investigate how compliance with non-pharmaceutical interventions in population affected the dynamics of the COVID-19 pandemic. 

“We faced many challenges as many of the pandemic's characteristics were unknown in February-March 2020," said Sheryl Chang, who led the study and is a PhD candidate. 

“Calibration is always vital in agent-based modelling (models which simulate the actions and interactions of individuals or organisations)."

“The tipping point identified for effective social distancing level (80 percent) is the highlight of this study. It is also striking that an earlier introduction of social distancing, even by just three days, could make a significant difference in epidemic containment, lengthening the duration of suppression measures by over three weeks on average.

“Several COVID-19 vaccines are now rapidly being developed. We plan to investigate how different vaccination approaches would affect the epidemic dynamics. This will shed some light on optimal vaccination priorities and strategies for Australia.

Agent-based modelling allows for highly detailed simulations

“Agent-based simulations allow us to model the pandemic and potential government interventions at the scale of individuals and households through to cities, states, and territories," said Dr Oliver Cliff. 

“These layered models enable key questions to be asked about the effect of local (suburb- or state-wide) and national restrictions on the public's health and economy.

“We believe that such high-resolution models are the way forward for policy making, facilitating quantitative analysis at each level of the state and national government.”

 

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