We spent the last post discussing principles of agent based modeling, and the basic layout of the model I have initially created. Now it's time to see some results! All of the code used to generate this model can be found here.
First off, I want to talk a bit about the approaches which I have taken towards the visualization of my model, as well as the inadequacies of these approaches. In the previous post, I discussed the fact that my model is based upon the usage of networks (graphs) for the mapping of social interactions. My first approach to visualization (particularly during the development process) was plotting these networks, as this allowed me to see how the disease was spreading in my model, and debug. This worked well during the testing phase, when my model population was rather small; however, when I scaled up the model, this technique did not work as well.
Two example small world networks generated and drawn by the networkx library in Python. As the number of nodes increases, ease of interpretation decreases dramatically. The graph on the right is often referred to as a hairball.
Network Animated GIFs
For now, I have harnessed a technique I have leveraged in a previous project in which I analyzed of US Senate data. I utilize the ImageMagick "convert" command-line tool in order to combine a series of static images into an animated gif (example here). For my network, I animate the nodes as they change color to indicate their infection state. The vizualization is a bit chaotic, but I think it looks real neat.
Animation displaying the spread of the disease through the network (# nodes = 1000). Each node (or individual) can have one of four states for a given pathogen: susceptible (blue), infected (yellow/orange), recovered (green), or dead (red). Here we show a single pathogen system.Read more...