Non-centred Bayesian inference for discrete-valued state-transition models: the Rippler algorithm

TL;DR

The paper introduces the Rippler algorithm, a non-centred Bayesian inference method for high-dimensional, discrete state-transition models in infectious disease transmission, improving estimation accuracy as model complexity grows.

Contribution

It presents a novel non-centred MCMC algorithm applicable to any individual-based state-transition model, enhancing inference performance over existing methods.

Findings

Rippler outperforms existing inference methods as the number of disease states increases.

The algorithm effectively estimates model parameters and unobserved disease statuses.

Performance improves with model complexity, demonstrating scalability.

Abstract

Stochastic state-transition models of infectious disease transmission can be used to deduce relevant drivers of transmission when fitted to data using statistically principled methods. Fitting this individual-level data requires inference on individuals' unobserved disease statuses over time, which form a high-dimensional and highly correlated state space. We introduce a novel Bayesian (data-augmentation Markov chain Monte Carlo) algorithm for jointly estimating the model parameters and unobserved disease statuses, which we call the Rippler algorithm. This is a non-centred method that can be applied to any individual-based state-transition model. We compare the Rippler algorithm to the state-of-the-art inference methods for individual-based stochastic epidemic models and find that it performs better than these methods as the number of disease states in the model increases.