Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data.

Pettitt, Anthony and Forrester, M. and Gibson, G. (2007) Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data. Biostatistics, 8 (2). pp. 383-401. ISSN 1468-4357

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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.

Item Type:
Journal Article
Journal or Publication Title:
Biostatistics
Additional Information:
RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
?? BAYESIAN INFERENCEFALSE NEGATIVESIMPERFECT DETECTABILITYINFECTIOUS DISEASESMARKOV CHAIN MONTE CARLO METHODSMRSAREVERSIBLE JUMP METHODSSCREENINGSENSITIVITYSTAPHYLOCOCCUSSTOCHASTIC EPIDEMIC MODELSSTATISTICS AND PROBABILITYSTATISTICS, PROBABILITY AND UNCERTA ??
ID Code:
2457
Deposited By:
Deposited On:
29 Mar 2008 12:17
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Sep 2023 00:20