Plotting progress

If it weren’t for visualization, statistics would be pretty boring. One of the ways I like to celebrate project milestones is by producing plots. This gives my left brain a break from the programming and mathematics  involved in my work and hands the reins over to the right side to consider visualization aesthetics.

I am currently developing a statistical package for Julia which provides simulation and inference tools for infectious disease outbreaks. Specifically, I am motivated by scenarios in which disease surveillance includes the collection of pathogen sequence information. The github repository is far from being in any shape for sharing, but I have gotten to a point where I can simulate the simultaneous evolution and transmission of a pathogen through a population (with a great deal of flexibility in the dynamics of both). This seemed as good as time as any to make an animation of a simple outbreak. The animation is made up of 1000 individual frames (50ms each). The frames were generated with Gadfly.jl, and combined through the command line with ImageMagick.

Simulated disease outbreak
The disease states of individuals through space and time are shown with coloured points. Disease transmission pathways are shown by black lines connecting individuals. If a pathway is not draw to an individual, but they become exposed to the disease, this indicates their exposure is due to disease pressure outside of our spatially modelled population. S, E, I indicate susceptible, exposed, and infectious disease states respectively. S* indicates a modified susceptible state in which susceptibility is some function of individual’s covariate information and infection history. In this simple case, an individual gains full immunity to all strains of the pathogen after a single exposure (equivalent to an SEIR model).

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