This will be an online event only. Zoom link will be emailed to registrants on the day of the event. Daylight savings are in place - webinar starts 12pm BNE time / 1pm SYD/CBR/MEL time

This webinar series is brought to you by the QUT Centre for Data Science and the Queensland Academy of Arts and Science.  They will explore what data and data science can tell us about COVID19 and vaccines

Moderator:

• Distinguished Professor Kerrie Mengersen - Director, QUT Centre for Data Science

Panelists:

• Dr Kirsty Short - Research Fellow, Head of Viral Parthenogenesis Lab, The University of Queensland Data Science in the News: COVID19 and vaccines
• Professor Raja Jurdak - Professor of Distributed Systems and Chair in Applied Data Science, Queensland University of Technology
• Associate Professor Dan Nicolau - Australian Research Council Future Fellow, Queensland University of Technology
• Dr Adrianne Jenner - Lecturer in Mathematical Oncology, Queensland University of Technology

More about the Panel Session Topics:

Dr Kirsty Short: COVID-19: A year later

Professor Raja Jurdak: COVID-19 vaccination strategies and importation risks

This talk will discuss our recent work on COVID-19 place-based vaccination strategies and importation risk. Because vaccine supplies are limited, efficient use of available vaccines to minimise COVID-19 spread is critical. I will describe our placed-based vaccination strategy that prioritises individuals visiting the busiest locations for vaccination, which can maximise reductions in spread. I will also cover our recent work on understanding COVID-19 importation risk into Australia from overseas based on incoming travel patterns.

Dr Adrianne Jenner: What modelling the immune response to SARS-CoV-2 infections can tell us about COVID-19

After infection with SARS-CoV-2, the primary distinction between whether asymptomatic, mild or severe COVID-19 disease will develop depends on an individual's immune response; however, understanding exactly what variation in the immune system drives the diversity of disease outcomes across the human population is challenging. Fortunately, as the disease has evolved in real-time, so has the human-based data available for scientists to leverage and build an understanding of this disease. For the past year, we have been undertaking a bilateral approach to investigate what happens inside the human body after SARS-CoV-2 infection by considering the immune response at the lung tissue level and the systemic level (whole body response) using mathematical and computational modelling. Leveraging the abundance of available of data, we have extrapolated a population of virtual patients for which we have identified biomarkers that drive the severity in COVID-19. Using these mathematical and computational models, we hope to now investigate how vaccine pressure may result in mutant strains and predict how an individual's immune response to the vaccine may vary, given the already varied responses to SARS-CoV-2 infections, and what this could mean for vaccine success.