2018

HOW-TO: Create a MATLAB Virtual Machine

A steadily-increasing number of students and postdocs in our group find themselves needing to run huge numbers of model simulations, and the path of least resistance — using their local desktop or laptop — simply isn't feasible. Thankfully we have the Nectar Cloud at our disposal! I've been helping several people in our group transition to this platform, and this has prompted me to prepare several step-by-step tutorial for the different needs I've encountered so far. It's my intention to make these all available online, and the first cab off the rank is: How to create a MATLAB VM.

Live coding, Emacs, and ghci

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.

The obvious choice of tool for live demonstrations of Haskell code and expression evaluation is ghci. And I happen to have ghci already installed, by virtue of using xmonad to manage my windows and workspaces. I also spend most of my working hours living in Emacs, which has great support for working with interactive programming environments (also referred to as REPLs, Read-Eval-Print Loops) and for taking code blocks from open files and evaluating them in these environments. So I know what my preferred tools are. But it wasn't immediately clear to me what the precise workflow should be.

No single-biggest roadblock

"It is difficult to make predictions, especially about the future" — this famous quote is a great opening line when discussing infectious disease forecasting. I used it in my talk at the Australian Influenza Symposium (1-2 Nov 2017), as did a speaker in an earlier session. And there are many reasons why infectious disease forecasting is hard. There isn't a single-biggest roadblock to making accurate epidemic forecasts, and I was recently quoted about this very matter in "How Bad Will the Flu Season Get? Forecasters Are Competing to Figure it Out" (The Scientist, 28 Nov 2017).

I really enjoy the challenge of engaging with non-academic audiences, both as an end in itself, and also because it should help me improve my grant writing. And this isn't my first experience with the media. I've previously been quoted in articles about the link between climate and influenza transmission (Flu outbreaks are subject to humidity — not just heat, Cosmos, 1 Nov 2016), and our collaboration with the Victorian Department of Health and Human Services (Defence scientists' bio attack detector could predict flu outbreaks, Herald Sun, 14 Jul 2016).

Epidemic forecasts as a tool for public health

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.