Approximate Bayesian Computation (ABC) is a likelihood-free (sometimes called simulation) method of inference. In this project I present a series of ABC methods which can be used to make approximate inference for epidemic models, and demonstrate their effectiveness on a series of simple case study outbreak datasets. Following this, I apply a selection of the methods to a complex spatio-temporal outbreak dataset for Sugarcane Yellow Leaf Virus. Finally, I discuss some questions that have arisen throughout the project on the use of ABC, and give my judgement on its position as an alternative to MCMC for making inference on epidemic models.

This project was the dissertation thesis of my Masters in Statistics.