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Using approximate Bayesian computation to estimate transmission rates of nosocomial pathogens

Drovandi, Christopher C. & Pettitt, Anthony N. (2011) Using approximate Bayesian computation to estimate transmission rates of nosocomial pathogens. Statistical Communications in Infectious Diseases, 3(1).

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Abstract

In this paper, we apply a simulation based approach for estimating transmission rates of nosocomial pathogens. In particular, the objective is to infer the transmission rate between colonised health-care practitioners and uncolonised patients (and vice versa) solely from routinely collected incidence data. The method, using approximate Bayesian computation, is substantially less computer intensive and easier to implement than likelihood-based approaches we refer to here. We find through replacing the likelihood with a comparison of an efficient summary statistic between observed and simulated data that little is lost in the precision of estimated transmission rates. Furthermore, we investigate the impact of incorporating uncertainty in previously fixed parameters on the precision of the estimated transmission rates.

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ID Code: 50265
Item Type: Journal Article
Keywords: approximate Bayesian computation, likelihood-free inference, Methicillin-resistant Staphylococcus aureus, Markov process, nosocomial pathogen, sequential Monte Carlo
DOI: 10.2202/1948-4690.1025
ISSN: 1948-4690
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Walter de Gruyter GmbH & Co. KG
Copyright Statement: The final publication is available at www.degruyter.com
Deposited On: 15 May 2012 08:48
Last Modified: 01 Aug 2013 07:58

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