In January I’m going to a workshop on category theory for modeling, with a focus on epidemiology.
• Formal scientific modeling: a case study in global health, 2026 January 12-16, American Institute of Mathematics, Pasadena, California. Organized by Nina Fefferman, Tim Hosgood, and Mary Lou Zeeman.
It’s sponsored by American Institute of Mathematics, the NSF, the Topos Institute, and the US NSF Center for Analysis and Prediction of Pandemic Expansion. Here are some of the goals:
1. Get a written problem list from a bunch of modelling experts, i.e. statements of the form “I’ll be interested in categorical approaches to modelling when they can do X”, or “how would category theory think about this specific dynamical behaviour, or is this actually not a category theory question at all?”, or … and so on.
2. Make academic friends. There will be people who are not at all category theorists (many of them haven’t even heard of the subject) but who have elected to spend 5 days at a working conference to actually work with some category theorists.
3. There will probably be a lot of conversations that are essentially 5–15 minute speed tutorials in “what is agro-ecology”, or “how do diabetes models work”, or “what does it mean to implement climate databases in a non-trivial way”.
I think looking at examples of existing successful collaborations between category theorists and modelers will help this meeting work better. I’m hoping to give a little talk about the one I’ve been involved in.
I really had very little idea how category theory could actually help modelers until Nate Osgood, Xiaoyan Li, Kris Brown, Evan Patterson and I spent about 5 years thinking about it. We used category theory to develop radically new software for modeling in epidemiology. It was crucial that Nate and Xiaoyan do modeling for a living, while Kris and Evan design category-based software for a living. And it was crucial that we worked together for a long, long time.
But I’m hoping that what we learned can help future collaborations. I’ve written up a few insights here:

Posted by John Baez 



