The course will take place in London, UK.

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 make it possible 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 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 two short courses Modern Techniques in Infectious Disease Modelling and Model Fitting and Inference for Infectious Disease Dynamics are offered back-to-back across two consecutive weeks. While either course can be taken on its own, students who take both courses will benefit from a comprehensive introduction to modern methods in infectious disease transmission modelling.