Dr Rob Moss

Research Fellow
Centre for Epidemiology and Biostatistics
Melbourne School of Population and Global Health
The University of Melbourne
Office 335
207 Bouverie St
(03) 8344 9430
rgmoss AT unimelb edu au

Mathematical modelling of biological and physiological systems:
• Infectious disease epidemics to inform health-care policy (details); and
• Neurohormonal regulation of renal water and sodium excretion (details).

Live coding, Emacs, and ghci

Teaching posts Feb 19, 2018 Tutorial

This semester I'm co-lecturing Declarative Programming (COMP90048). The topics I'll be covering include monads, laziness, performance, and type system expressiveness, with Haskell as our language of choice. This will be the first time that I'll try live coding in front of students, because I've previously lectured non-programming subjects such as multi-variable calculus and infectious disease modelling.

Epidemic forecasts as a tool for public health

Influenza posts Jan 4, 2018 Forecasting Influenza

Infectious disease forecasting has been a very active research area in the past few years, and these methods have the potential to provide valuable decision-support capabilities for public health staff. There are many challenges that must be surmounted before this can be realised, and one major gap in the literature is operational research in pilot, real-world applications. We report on exactly this kind of study in our most recent forecasting paper, “Epidemic forecasts as a tool for public health: interpretation and (re)calibration”, which has just been made available online and will appear in an upcoming issue of the Australian and New Zealand Journal of Public Health.

Disease surveillance: no silver bullet

Influenza posts Nov 3, 2017 Forecasting Influenza

In thinking about the strengths and limitations of different surveillance systems — from the perspective of infectious disease forecasting in my case, but the point applies more generally — it becomes clear that there is no silver bullet. No one surveillance system will tell us everything we need to know in order to understand the current impact of a disease on a population, or to predict the future impact of a disease. The surveillance pyramid model illustrates this in a very nice and clear manner: