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

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Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data. / Pettitt, Anthony; Forrester, M.; Gibson, G.
In: Biostatistics, Vol. 8, No. 2, 04.2007, p. 383-401.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Pettitt, A, Forrester, M & Gibson, G 2007, 'Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data.', Biostatistics, vol. 8, no. 2, pp. 383-401. https://doi.org/10.1093/biostatistics/kxl017

APA

Pettitt, A., Forrester, M., & Gibson, G. (2007). Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data. Biostatistics, 8(2), 383-401. https://doi.org/10.1093/biostatistics/kxl017

Vancouver

Pettitt A, Forrester M, Gibson G. Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data. Biostatistics. 2007 Apr;8(2):383-401. doi: 10.1093/biostatistics/kxl017

Author

Pettitt, Anthony ; Forrester, M. ; Gibson, G. / Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data. In: Biostatistics. 2007 ; Vol. 8, No. 2. pp. 383-401.

Bibtex

@article{e6fccfd7064a454b83613af22b4d2b03,

title = "Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data.",

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.",

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

author = "Anthony Pettitt and M. Forrester and G. Gibson",

note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",

year = "2007",

month = apr,

doi = "10.1093/biostatistics/kxl017",

language = "English",

volume = "8",

pages = "383--401",

journal = "Biostatistics",

issn = "1468-4357",

publisher = "Oxford University Press",

number = "2",

}

RIS

TY - JOUR

T1 - Bayesian inference of hospital-acquired infections and control measures given imperfect surveillance data.

AU - Pettitt, Anthony

AU - Forrester, M.

AU - Gibson, G.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2007/4

Y1 - 2007/4

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - False negatives

KW - Imperfect detectability

KW - Infectious diseases

KW - Markov chain Monte Carlo methods

KW - MRSA

KW - Reversible jump methods

KW - Screening

KW - Sensitivity

KW - Staphylococcus

KW - Stochastic epidemic models

U2 - 10.1093/biostatistics/kxl017

DO - 10.1093/biostatistics/kxl017

M3 - Journal article

VL - 8

SP - 383

EP - 401

JO - Biostatistics

JF - Biostatistics

SN - 1468-4357

IS - 2

ER -

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