With this short course we aim to bridge the gap between statistical inference methods and training in infectious disease modelling. We will introduce key terminology from frequentist and Bayesian approaches as well as methods ranging from Markov-Chain Monte Carlo (MCMC) to particle filters and Approximate Bayesian Computation (ABC).
Our aim is to equip students and researchers using infectious disease models with the theoretical background needed to better understand the literature, as well as with practical knowledge of the tools available to apply these methods in their own research.
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.
For more information, please view the LSHTM course website.
