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Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data

Cassidy, Rosanna; Kypraios, Theodore; O'Neill, Philip D.

Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data Thumbnail


Authors

Rosanna Cassidy

PHILIP O'NEILL PHILIP.ONEILL@NOTTINGHAM.AC.UK
Professor of Applied Probability



Abstract

Whole genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years there have been numerous new methods and models developed to exploit such high-resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this paper we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. 1 Although the methods are generic in nature, we focus specifically on nosocomial pathogens, and analyse a data set collected during an outbreak of MRSA in a hospital setting.

Citation

Cassidy, R., Kypraios, T., & O'Neill, P. D. (2020). Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data. Statistics in Medicine, 39(12), 1746-1765. https://doi.org/10.1002/sim.8510

Journal Article Type Article
Acceptance Date Jan 31, 2020
Online Publication Date Mar 6, 2020
Publication Date May 30, 2020
Deposit Date Feb 4, 2020
Publicly Available Date Mar 7, 2021
Journal Statistics in Medicine
Print ISSN 0277-6715
Electronic ISSN 1097-0258
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 39
Issue 12
Pages 1746-1765
DOI https://doi.org/10.1002/sim.8510
Keywords Statistics and Probability; Epidemiology
Public URL https://nottingham-repository.worktribe.com/output/3881573
Publisher URL https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8510

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