Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data

A. N. Pettitt, M. L. Forrester, G. J. Gibson

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

This paper describes a stochastic epidemic model developed to infer transmission rates of asymptomatic communicable pathogens within a hospital ward. Inference is complicated by partial observation of the epidemic process and dependencies within the data. The epidemic process of nosocomial communicable pathogens can be partially observed by routine swabs testing for the presence of the pathogen. False-negative swab results must be accounted for and make it difficult to ascertain the number of patients who were colonized. Reversible jump Markov chain Monte Carlo methods are used within a Bayesian framework to make inferences about the colonization rates and unknown colonization times. The methods are applied to routinely collected data concerning methicillin-resistant Staphylococcus Aureus in an intensive care unit to estimate the effectiveness of isolation on reducing transmission of the bacterium.

Original languageEnglish
Pages (from-to)383-401
Number of pages19
JournalBiostatistics
Volume8
Issue number2
DOIs
Publication statusPublished - Apr 2007

Keywords

  • Bayesian inference
  • False negatives
  • Imperfect detectability
  • Infectious diseases
  • Markov chain Monte Carlo methods
  • MRSA
  • Reversible jump methods
  • Screening
  • Sensitivity
  • Staphylococcus
  • Stochastic epidemic models

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