An open course resource for learning about model fitting and inference for infectious disease dynamics, using Julia and the Turing.jl probabilistic programming framework.
Fitting mechanistic models to infectious disease data is an important use of models, but the range of available methods can make it hard to know where to start. This course covers methods from MCMC to particle filters and approximate Bayesian computation, with practical sessions throughout. Additional sessions cover variational inference and universal differential equations (combining mechanistic models with machine learning). If you have experience programming in R, Python, or a similar language, you have everything you need to get started. No prior Julia experience is required.
This is an annual short course, taught over four days at the London School of Hygiene & Tropical Medicine. The session timetable shows how the week runs.
Interested in attending the 2027 edition at LSHTM? Register your interest on the LSHTM course page to receive updates.
Everything the course uses is here, so you can work through it on your own. You are welcome to use the material in your own teaching too. Tell us how you got on, or ask about anything in the sessions, on the Discussion board. If you find something wrong in the material, open an issue so it can be fixed.
All materials here are provided under the permissive MIT License.