Doherty Institute and University of Melbourne modelling guiding COVID-19 response

AMSI members are the forefront of the complex modelling behind the Government’s response to this deadly coronavirus pandemic, with mathematicians creating models of the population to predict the course and impact of the virus.

But how does this work, and why do governments act upon such predictions?

Professors James McCaw and Jodie McVernon of the University of Melbourne / Doherty Institute provided an insight into their COVID-19 modelling to ABC News today.

Professor McCaw said “they put some ‘very scary numbers’ to the Government early on, and they were not dismissed. We don’t have an overwhelmed hospital system yet, and we may well never have one if we continue to base our responses on the best available data.”

Professors McCaw and McVernon both feature in the AMSI Maths Delivers video: ‘The Spread of Disease’ providing an insight into the mathematical modelling of susceptibility, infection spread and recovery of disease in populations.

James McCaw is a mathematical biologist and infectious diseases epidemiologist with a PhD in theoretical physics from the University of Melbourne.  He holds a joint appointments at the University of Melbourne in Mathematics and Statistics within the Faculty of Science (alongside his role as Associate Dean – Research) and at the University’s School of Population and Global Health

Professor McCaw’s interests span from modelling host-pathogen-drug dynamics, focusing on influenza and malaria, to developing public health control strategies for emerging and re-emerging infectious diseases. One of his current projects is Household Transmission Investigation for Novel Coronavirus (COVID-19) Infection Aka Ffx Full Implementation.

Professor Jodie McVernon is a physician with subspecialty qualifications in public health and vaccinology based at the Doherty Institute. Jodie possesses extensive expertise in clinical vaccine trials, epidemiologic studies and mathematical modelling of infectious diseases. These focus on the application of a range of cross-disciplinary methodological approaches including mathematical and computational models, to synthesise insights from basic biology, epidemiological data and sociological research. Such models advance understanding of the observed epidemiology of infectious diseases and inform understanding of optimal interventions for disease control.