Model Fitting and Inference for Infectious Disease Dynamics

A short course taught by members of the Centre for the Mathematical Modelling of Infectious Diseases.

There is a growing demand for mathematical modellers in public health to explain observed disease trends and predict the outcome of interventions, often by synthesising information from different data sources. At the same time, increasing computational power and methodological advances are providing exciting opportunities to fit ever more complex mechanistic models to data. In light of the speed of methodological advances and the broad nature of the field, the task of choosing from the available methods and packages, as well as putting them into practice, can be daunting.

With this short course 'linking theory to data: modern methods for fitting models of infectious disease dynamics', we aim to bridge the gap between state-of-the-art 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).