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arXiv · 2602.10924

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

James Neill·Lloyd A. C. Chapman·Chris Jewell
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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.

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James Neill, Lloyd A. C. Chapman, Chris Jewell. 2026-02-11. Non-centred Bayesian inference for discrete-valued state-transition models: the Rippler algorithm. https://arxiv.org/abs/2602.10924

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