COVID-19 modelling and risk analysis

Research program: 
Currently recruiting: 
Yes
The challenge: 

Rapid risk analysis and predictive modelling can assist with policy and practice in epidemic control such as the COVID-19. We have developed a transmission model for COVID-19 and risk analysis frameworks for infectious diseases that can be used to answer a range of urgent policy questions, with customised models for most countries in Asia and the Pacific. We also work on influenza, smallpox, anthrax and other epidemic infections.

The project: 

Early in the COVID-19 pandemic we modelled the impact of the travel ban Australia implemented on China. We looked at scenarios of partial and complete lifting of the ban, related to the incidence of disease in China and the travel patterns. Then we focussed on supporting policy in NSW for COVID-19 vaccination logistic distribution in scenarios with limited and full vaccine supplies, and showing the potential impact of a COVID-19 outbreak in countries recently affected by measles epidemics, like Samoa. We have since done work on regional disease control, school closures, and social distancing strategies with colleagues at Arizona State University within the PLuS Alliance, and on universal face mask use. We used mathematical deterministic models of COVID-19 to estimate the impact of universal masks, using the second wave in Victoria, as well as estimating its impact in NYC and on a university campus in Mississippi, to inform policy on reopening. Additional studies underway have examined the impact of vaccination uptake and waning impact. Risk analysis frameworks include the EPIRISK and Flucast designed for real-time risk prediction of identified disease events having a serious outcome, to prioritise response and urgency of intervention. Another risk analysis tool, the modified Grunow-Finke assessment tool (mGFT), was developed to differentiate natural and unnatural epidemics.

The method: 

A variety of models have been used to observe transmission dynamics of COVID-19. A SEIR (Susceptible - Exposed - Infectious - Recovered) model and an agent-based model for COVID-19 transmission was developed by the Kirby Institute’s Biosecurity Program to undertake the modelling and risk analyses mentioned above. The EPIRISK framework can be further enhanced and validated against COVID-19 using machine learning approach. The origin of COVID-19 will be analysed using the mGFT. The web and mobile applications of risk analysis tools will be developed for users to get results automatically. 

The results: 

Our published study on travel bans showed the travel ban was highly effective for containing the COVID-19 epidemic in Australia in early 2020 during the epidemic peak in China and averted a much larger epidemic while COVID-19 was largely localised to China. This research demonstrates the effectiveness of travel bans applied to countries with high disease incidence and can inform decisions on placing or lifting travel bans during the COVID-19 epidemic. The other studies show that physical distancing and mask use are effective in controlling COVID-19 and that countries affected by a recent measles outbreak could face a worse outcome from a COVID-19 due to persistent immune amnesia from a measles infection. We have a continual pipeline of modelling as part of our EPIWATCH observatory, including models of smallpox re-emergence used to underpin our Pacific Eclipse pandemic exercise. We are currently modelling the impact of the was on COVID-19 in Ukraine.

The impact: 

We are able to provide rapid decision support for key policy questions around control of COVID-19, influenza and other epidemic infections.

Project contact: 
Head, Biosecurity Program, and Professor of Global Biosecurity, Kirby Institute and NHMRC Principal Research Fellow
Project collaborators: 
Header image credit: 

Events