Several months ago I heard a fascinating segment on NPR about how astronomers process data by turning it into sound. I thought I knew my data inside and out, but never had I conceptualized it as sound. I was so taken with the idea that I gave it a shot.
The raw data are a record of every person who has been diagnosed with Middle East Respiratory Syndrome coronavirus (MERS), maintained by Andrew Rambaut. About 40% of the records include some identification of cluster membership - in other words, people who may be epidemiologically linked. I used my Python package epipy to algorithmically reconstruct transmission trees from those records. I call them case trees, and you can learn more about them here. Case trees aren't perfect, but they are a plausible approximation of how the disease was spread from person to person.
The next step was to borrow heavily from the example code in cirlab's miditime package. Using the networks generated for the case tree plot, I transformed the outbreak into sound. One outbreak year corresponds to five seconds in the song. Each case is a note; the higher the note on the scale, the further up the transmission tree it is. Days when there are several cases result in multiple notes played at once. You can hear that as the incidence increases in spring 2015, the sound gets quite hectic.
After generating a midi file, I used Garage Band to turn the sound into something resembling music by playing the track with two different instruments. Although the subject is quite grim, the resulting song is a fun 11 second rendition of an outbreak's course.
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