Whenever science is mentioned in the context of viruses and disease, it’s likely that the first image that comes into your mind is one of people in white coats surrounded by mysterious vials injecting who knows what into test tubes. Whilst this isn’t an entirely inaccurate depiction of how science is used to create vaccines or other medicines, it is by no means the only way that science can be used to fight disease and help people be as healthy as possible.
My project over the last six odd weeks was to code a model that simulated how a virus (I’ll leave it up to you to guess which virus that made it rounds through the world in 2020 inspired this) spreads through a population. It turns out there are a lot of questions about the different ways that viruses spread and what the best way to tackle viruses with these different spreading characteristics, that can be studied using nothing but a laptop and a bunch of time. We looked into things like, is it more important to be able to trace a high percentage of infected people or is it better to be able to keep track of a large number of infections but with a lower efficiency. We also investigated if our answer was the same for a superspreading virus as it was for a non-superspreading virus. In some respects, simulations are the only way that we can study some of these questions since it’s not like we can wait for the next global pandemic to put our theories into practice.
Going into this project I had no experience in disease modelling, little experience with coding and not much of an end goal for the project. However, one of the most fulfilling aspects about doing research like this are all the new things you learn and how well you remember them when you are learning with a clear objective or application for that knowledge. Another really enjoyable aspect is the freedom to go in (almost) whatever direction you want, to try and add new layers to your model or try to apply new theorems to the problem.
La Trobe University