Lloyd Chapman (Lancaster University) – Non-centered Bayesian inference for individual-level epidemic models: the Rippler algorithm

Infectious diseases are often modelled via stochastic individual-level state-transition processes. As the transmission process is typically only partially and noisily observed, inference for these models generally follows a Bayesian data augmentation approach. However, standard data augmentation Markov chain Monte Carlo (MCMC) methods for individual-level epidemic models are often inefficient in terms of their mixing or challenging to implement. In this talk, I will introduce a novel data-augmentation MCMC method for discrete-time individual-level epidemic models, called the Rippler algorithm. I will explain how the Rippler algorithm works and how its performance compares to the standard and the state-of-the-art inference methods for individual-level models. I will also present results of application of the algorithm to data on AMR E. coli from Malawi.