February 4, 2020
Mathematical Epidemiologist, Dr Trish Campbell explains how infectious disease modelling can help researchers to understand why some communities are prone to particular infectious diseases.
What is your role at the University of Melbourne and the Victorian Infectious Diseases Reference Laboratory (VIDRL)?
I’m a Research Fellow at The Peter Doherty Institute for Infection and Immunity. Part of my role is to analyse infectious disease data and use the findings to build mathematical and computational models of how diseases spread through populations. We can then use these models to understand what is likely to happen if control measures, such as vaccination or mass drug administration, are introduced. At the University of Melbourne, I coordinate and teach subjects on infectious disease modelling and One Health as well as supervise student projects.
What attracted you to epidemiology and mathematical modelling?
I’ve always loved mathematics and following a career change, I decided to pursue my interest through a Bachelor of Science. My intention was to become a cryptographer until, by chance, I attended a presentation on infectious disease modelling given by a visiting Research Fellow from South Africa. Using mathematics to control disease was the coolest use of mathematics I’d ever seen, and I was hooked! It gives me great satisfaction to think that the work I do has the potential to improve lives.
What is the current work you are doing related to APPRISE?
One of the critical aspects of managing emerging infectious diseases is having capacity at hand when it is needed. One of my roles has been to provide training and development opportunities for PhD students and early career researchers who develop infectious disease models for endemic infections. By supervising and mentoring the next generation of researchers, I am building capacity for modelling emerging infectious diseases so that this resource is available when it is required.
How does mathematical modelling help researchers to improve the ability of communities to prepare for, or respond to, emerging infectious diseases?
Mathematical models provide a framework to understand how diseases spread and might be controlled, but they can be time-consuming to develop, and so we need to have models ready to go in case of emergency. When communities are faced with an emerging infectious disease, making decisions about control measures can be challenging as there is often limited (and rapidly changing) information available on which to base decisions.
Researchers can prepare for these situations in advance, using mathematical modelling to identify how hypothetical diseases with different characteristics spread in the community and the likely impact of different interventions. As an epidemic unfolds and more is known about the emerging infectious disease (like how quickly it is spreading, or how severe the symptoms are), researchers can match this information with the previously modelled hypothetical diseases, to better predict the likely impact of control measures in real-time.
What has been the impact on your career and/or research of the joint-early-career researcher meetings for centres of research excellence involved in fields associated with infectious diseases?
I’ve been involved in setting up a group of early- and mid-career researchers working across several infectious diseases-focused centres of research excellence, including APPRISE. We run a communication channel for the group using the ‘Slack’ platform, which allows us to spread the word about any conferences or funding calls and to share training materials across our network.
One of the aspects of this group that I’ve found particularly useful is being able to attend early- and mid-career professional development sessions run by other CREs. Last year’s session run by the Centre of Research Excellence in Emerging Infectious Diseases (CREID) on ‘Strategies for raising your profile and creating a greater impact of your research’ inspired me to put more time into spreading the word about the work that I do.