Related Experiment Video
Updated: Aug 24, 2026
Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice
Published on: October 10, 2025
Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in
James Neill1, Rebecca Lester2, Winnie Bakali3
1School of Mathematical Sciences, Lancaster University, Lancaster, LA1 4YF UK.
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.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s11222-026-10956-z.
