Course objectives
Teaching methods and course materials
The course consists of computer practical sessions interspersed with introductory lectures. The practical sessions will use Julia (with the Turing.jl probabilistic programming framework), a programming language designed for scientific computing. No prior Julia experience is required, but some pre-reading of Julia basics and concepts is encouraged for those who are able. The course materials include guidance for those coming from R throughout. Participants should bring their own laptop if at all possible.
The course lasts three and a half days:
- Day 1: we will introduce the conceptual underpinning of inference using dynamic models and apply this to model fitting using deterministic models.
- Day 2: we will cover MCMC diagnostics, model checking and validation, and introduce stochastic models.
- Day 3: we will implement particle-based methods for matching stochastic models to data.
- Day 4 (half day): we will cover observation models and Approximate Bayesian Computation.
Additional materials on variational inference and universal differential equations are available for self-study.
Who is the course for?
This course is aimed at students and researchers who are working with dynamic models of infectious disease (i.e., broadly based on the SIR model) and would like to learn about modern methods of matching these to data. The course is limited to 24 participants.
Prerequisites
In order to benefit from the course, basic knowledge of infectious disease modelling is required. Participants are expected to have a good understanding of key concepts outlined in Introduction to Infectious Disease Modelling and Its Applications or Modern Techniques in Infectious Disease Modelling.
Some experience with a programming language (R, Python, MATLAB, or similar) is expected. No prior knowledge of Julia is required. The course materials provide all the guidance needed to get started.
Testimonial
“The Model Fitting and Inference for Infectious Disease Dynamics course was the perfect follow on from my basic prior understanding of modelling infectious disease dynamics. The course materials were clear and well-constructed, and the coordinators and session leads were helpful and clear. I would highly recommend this short course for anyone looking to get a hands on understanding of modelling infectious disease dynamics." - Em Prestige, 2023