For the past few months I’ve been collaborating with Jack Iwashyna, Assistant Professor at UofM’s medical school and SI MSI student Umanka Hebbar Karkada. Jack had a fun idea, and a fun data set – hospital to hospital patient transfers, mined from medicare claims. These transfers are a way for highly resistant infections to jump from one critical care unit to another. Mostly hospitals devote resources to preventing infection spread separately from one another.
We posed the question of how resources could be allocated in a coordinated way to maximally stem the spread of infection. Umanka and I tried several stategies – targeting hospitals with the highest degree (number of hospitals they trade patients with), highest betweenness (they are on the “path” between other hospitals), and a greedy allocation based on the number of beds infected at each hospital and downstream from that hospital.
The results are here. Both figures show hospitals as nodes sized by the number of ICU beds they have.
This shows the number of resources allocated by hospital (gray = none, blue = few, red = many).

This shows the relative benefit of a random allocation vs. targeting particular hospitals. (blue = hospital unlikely to become infected, red = likely to be infected)

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