Evidence map›Paper›PMID 42630287›Full record

ArticleStatistics and computing2026

Non-centering for discrete-valued state transition models: an application to ESBL-producing

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

Abstract read
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In one paragraph

Article in Statistics and computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

James NeillSchool of Mathematical Sciences, Lancaster University, Lancaster, LA1 4YF UK.
Rebecca LesterDivision of Infection and Immunity, University College London, Gower Street, London, WC1E 6BT UK.
Winnie BakaliMalawi-Liverpool Wellcome Research Programme, Kamuzu University of Health Sciences, Blantyre, Malawi.
Gareth RobertsDepartment of Statistics, University of Warwick, Coventry, CV4 7AL UK.
Nicholas FeaseySchool of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF UK.
Lloyd A C ChapmanSchool of Mathematical Sciences, Lancaster University, Lancaster, LA1 4YF UK.
Chris JewellSchool of Mathematical Sciences, Lancaster University, Lancaster, LA1 4YF UK.

Funding

Wellcome Trust
6 · The paper itself

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 Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-026-10956-z.

Indexed as

Antimicrobially resistant bacteriaBayesian inferenceData-augmentationEpidemiologyIndividual-based modelMarkov chain Monte Carlo methods

Identifiers

PMID42630287
PMCPMC13493405

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.