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arXiv · 2504.11836

Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi

James Neill·Rebecca Lester·Winnie Bakali·Gareth Roberts·Nicholas Feasey·Lloyd A. C. Chapman·Chris Jewell
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Abstract

Infectious disease transmission is often modelled by discrete-valued stochastic state-transition processes. Due to a lack of complete data, Bayesian inference for these models often relies on data-augmentation techniques. These techniques are often inefficient or time consuming to implement. We introduce a novel data-augmentation Markov chain Monte Carlo method for discrete-time individual-based epidemic models, which we call the Rippler algorithm. This method uses the transmission model in the proposal step of the Metropolis-Hastings algorithm, rather than in the accept-reject step. We test the Rippler algorithm on simulated data and apply it to data on extended-spectrum beta-lactamase (ESBL)-producing E. coli collected in Blantyre, Malawi. We compare the Rippler algorithm to two other commonly used Bayesian inference methods for partially observed epidemic data, and find that it has a good balance between mixing speed and computational complexity.

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James Neill, Rebecca Lester, Winnie Bakali, Gareth Roberts, Nicholas Feasey, Lloyd A. C. Chapman, Chris Jewell. 2025-04-16. Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi. https://arxiv.org/abs/2504.11836

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