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Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

Proceedings of the National Academy of Sciences of the United States of America·2022
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Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data.

Rosanna Cassidy1, Theodore Kypraios1, Philip D O'Neill1

  • 1School of Mathematical Sciences, University of Nottingham, Nottingham, UK.

Statistics in Medicine
|March 7, 2020
PubMed
Summary
This summary is machine-generated.

Whole-genome sequencing aids infectious disease outbreak analysis by reconstructing transmission. This study introduces new modeling and assessment methods for genomic epidemiology, focusing on hospital-acquired infections like MRSA.

Keywords:
Bayesian methodsMCMCMRSAantimicrobial resistancewhole-genome sequences

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Area of Science:

  • Genomic epidemiology
  • Infectious disease outbreak analysis
  • Mathematical modeling

Background:

  • Whole-genome sequencing (WGS) offers high-resolution data for infectious disease outbreaks.
  • Conventional epidemiological data can be enhanced by WGS insights.
  • Existing methods for assessing genomic epidemiological models are underdeveloped.

Purpose of the Study:

  • To develop novel modeling and model assessment methods for genomic epidemiology.
  • To apply these methods to analyze a hospital outbreak of Methicillin-resistant Staphylococcus aureus (MRSA).

Main Methods:

  • Building upon established work (Worby et al.), new generic modeling techniques were created.
  • New methods for assessing the performance and reliability of these genomic models were developed.
  • Analysis focused on a real-world dataset from a hospital MRSA outbreak.

Main Results:

  • The study successfully developed and applied new methods for genomic data analysis in outbreak settings.
  • The introduced model assessment techniques provide a framework for evaluating genomic epidemiology tools.
  • The findings demonstrate the utility of WGS in understanding hospital-acquired infections.

Conclusions:

  • The developed methods enhance the analysis of infectious disease outbreaks using whole-genome sequencing data.
  • Robust model assessment is crucial for reliable genomic epidemiology.
  • This work contributes to better control strategies for hospital-acquired pathogens like MRSA.