HealthThis session runs from 1:00-2:30 and focuses on health data reporting and analysis, with a focus on Covid-19. Tentative speakers are:
- Talking about complicated things in uncomplicated ways
Casey Briggs, Data Journalist, ABC News
The pandemic ignited a dormant hunger for data among Australians, many of whom found themselves studying charts in a way they hadn’t looked at since leaving school. Never before had a daily set of graphs had such an influence on people’s lives and moods. I’ll share some of the lessons we’ve learned in communicating imprecise, imperfect, and impenetrable data to a general audience, and lift the hood on some of the tools we’ve used to do it.
- Model Madness: the difficulty of communicating and reporting on modelling
Liam Mannix, National science reporter for The Age and The Sydney Morning Herald
Modelling played a key role in our pandemic response. But how effectively was it communicated to the public? As a science reporter who did most of The Age and the Sydney Morning Herald's modelling reporting, my suspicion is: very poorly. There are several reasons for this that I think can inform how we go forward both as journalists and as modellers.
- Lessons learnt from developing modelling inside the public service
Michael Lydeamore, Monash University
During the COVID-19 pandemic, I took up a secondment to the Victorian Department of Health, to form and lead a modelling, forecasting and analytics team. This team provided key advice to senior government officials, ministers and health officers. During this, I learnt a huge amount of lessons on communication, investigation and project pitching. In this talk, I will cover some of the key models developed for state and national governments, how these were communicated, their relative impact, and the mistakes and lessons I have taken away from the experience. I will cover “scenario” modelling, which helps to understand what might be coming in the future, and the relative impact. These models tend to be data-informed (as opposed to data-driven), and can help to understand worst-case scenarios and the quantification of the risk associated with various policy positions. I will then cover the data-driven analytics, including quantifying immunity, near-term forecasting and spatial analytics, which inform short-term or day-to-day decisions.