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Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
Author(s) -
Cassidy Rosanna,
Kypraios Theodore,
O'Neill Philip D.
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8510
Subject(s) - inference , computer science , infectious disease (medical specialty) , whole genome sequencing , outbreak , bayesian inference , exploit , bayesian probability , computational biology , data mining , data science , genome , artificial intelligence , biology , disease , medicine , genetics , virology , gene , computer security , pathology
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 article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting.