Mathematical Modelling that Supported Australia and New Zealand’s COVID-19 Responses

Rebecca Chisholm

(La Trobe University, Australia)

"Modelling response strategies for potential COVID-19 outbreaks in remote Australian Aboriginal communities"

Remote Australian Aboriginal and Torres Strait Islander communities have potential to be severely impacted by COVID-19. Accordingly, the Aboriginal and Torres Strait Islander Advisory Group on COVID-19, co-chaired by the Australian Government Department of Health and the National Aboriginal Community Controlled Health Organisation led the development of specific guidance to support initial response to identified infections in these settings, and commissioned modelling to help inform this advice. We developed an individual-based model to represent remote communities of different sizes to consider alternative public health responses following the silent introduction of infection. The model included data-informed representation of extended family connections spanning multiple, often crowded dwellings, which are a key driver of infection spread. A range of strategies for case finding, quarantining of contacts, testing, and lockdown were examined. Our model suggests a SARS-CoV-2 outbreak will develop and spread rapidly in remote communities if an undetected infection is introduced. Prompt case detection with quarantining of extended-household contacts and a 14-day lockdown for all other residents, combined with exit testing for all, is the strategy most likely to achieve definitive initial containment.

Emily Harvey

(ME Research & Te Pūnaha Matatini, Aotearoa New Zealand)

"Modelling COVID-19 in Aotearoa NZ on a bipartite contact network of 5 million individuals"

Many of the models used for rapid policy advice during the COVID-19 pandemic have relied on simplifying assumptions about the homogeneity of individuals, however we know that risk factors for exposure, transmission, and poor outcomes are not evenly distributed across society. We have built a stochastic model of infection dynamics that runs on an empirically derived bipartite contact network of the ~5 million people in Aotearoa New Zealand. The contact network includes spatial information, and individual demographic information, along with distinct ‘transmission contexts’ including dwellings, workplaces, and schools, built from linked data in the Statistics NZ Integrated Data Infrastructure. This network is the underlying structure on which we run a stochastic contagion process to model the spread of COVID 19, which includes explicit representation of the testing and contact tracing processes. We have used this model to estimate the probable outcomes of COVID outbreaks in Aotearoa and to evaluate the effect of non-pharmaceutical interventions including 'Alert Level' changes. In particular, we find that this heterogeneity (network structure) means that the effect of different interventions does not combine linearly.

Michael Plank

(University of Canterbury, Aotearoa New Zealand)

"Modelling the risk of re-introduction of COVID-19 from border arrivals"

In an attempt to maintain the elimination of COVID-19 in New Zealand, all international arrivals are required to spend 14 days in government-managed quarantine and to return a negative test result before being released. We model the testing, isolation and transmission of COVID-19 within quarantine facilities to estimate the risk of community outbreaks being seeded at the border. We use a simple branching process model for COVID-19 transmission that includes a time-dependent probability of a false-negative test result. We show that the combination of 14-day quarantine with two tests is highly effective in preventing an infectious case entering the community, provided there is no transmission within quarantine facilities. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increases the risk of an infectious case being released. We calculate the fraction of cases detected in the second week of their two-week stay and show that this may be a useful indicator of the likelihood of transmission occurring within quarantine facilities. Frontline staff working at the border risk exposure to infected individuals and this has the potential to lead to a community outbreak. We use the model to test surveillance strategies and evaluate the likely size of the outbreak at the time it is first detected. We conclude with some recommendations for managing the risk of potential future outbreaks originating from the border.

Freya Shearer

(The University of Melbourne, Australia)

"Supporting the Australian response to COVID-19 through model-based situational assessment"

A key element of epidemic decision-making is situational awareness — that is, knowing the current and potential future status of the epidemic. Outputs from mathematical and statistical models have provided enhanced situational awareness to the Australian government throughout the course of the COVID-19 pandemic. Our response to COVID-19 required the rapid development of new methodologies and data streams for situational assessment, particularly with respect to monitoring changes in population behaviour and estimating transmission risk in the absence of cases. In this talk, I will describe Australia’s situational awareness modelling program for COVID-19. I will provide an overview of the modelling outputs reported to key government decision-making committees on a weekly basis (at least) since April 2020. Further, I will describe how our methods and the structure of our reporting has evolved over time, in response to changing epidemiology and response priorities.