Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)
Donald, Margaret, Mengersen, Kerrie L., Toze, Simon , Sidhu, Jatinder P.S. , & Cook, Angus (2011) Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA). Journal of Water and Health, 9(1), pp. 10-26.
Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.
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|Item Type:||Journal Article|
|Keywords:||MCMC, parameter uncertainty, Quantitative Microbial Risk Assessment (QMRA)|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > MEDICAL BIOCHEMISTRY AND METABOLOMICS (110100)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2011 IWA Publishing|
|Deposited On:||09 Mar 2011 10:07|
|Last Modified:||01 Mar 2012 00:31|
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