Login to
Secure login for NCZR members.
Tools and databases for securely exchanging research data.
Bayesian Approaches in Microbial Risk Assessment
Quantitative Microbial Risk Assessment (MRA) is an important tool for regulatory decision-making [1]. The need to incorporate uncertainty in inputs to MRA models is recognised (e.g. [2-3]), and many MRAs are carried out in a probabilistic Monte Carlo simulation framework (e.g. [4-5]).
Historically, attention has been paid to obtaining useful data to parameterise MRA models, and yet uncertainty in MRA models has often been incorporated arbitrarily. Available data can be poor, and expert opinion, often solicited informally, is used to describe uncertainty.
Furthermore, other types of uncertainty with potentially large effects, 
such as model uncertainty, may be ignored. Incomplete uncertainty analysis may be a source of social misunderstanding of risk.
The BAMRA group brings together individuals from diverse backgrounds with a common interest in improving the methods to quantify uncertainty in Microbial Risk Assessment models.
Our objectives are five-fold:
- To create an environment in which communication and knowledge transfer between environmental and health professionals working in MRA and statisticians can take place;
- To enhance understanding of Bayesian approaches in the MRA discipline, and identify areas of MRA in which the approaches could improve the quantification of uncertainty;
- To propose and start the development of novel Bayesian approaches;
- To build better models and increase understanding of how existing models operate through the use of Bayesian sensitivity analysis;
- To ensure that the outputs of the initiative reach a wide target audience.
BAMRA is funded by the Environment and Human Health Programme, which is a joint three-year inter-disciplinary capacity-building programme supported by NERC, EA, Defra, the MOD, MRC, The Wellcome Trust, ESRC, BBSRC, EPSRC and HPA.
Who are we?
The BAMRA group members are:
- Helen Clough, University of Liverpool
- Marc Kennedy, Central Science Laboratory
- Clive Anderson, University of Sheffield
- Gary Barker, Institute for Food Research
- Paul Cook, Food Standards Agency
- Andy Hart, Central Science Laboratory
- Pradeep Malakar, Institute for Food Research
- Jeremy Oakley, University of Sheffield
- Tony O'Hagan, University of Sheffield
- Annemarie Pielaat, RIVM (Governmental Institute for Public Health and Environment) Joost Smid, RIVM (Governmental Institute for Public Health and Environment)
- Emma Snary, Veterinary Laboratories Agency
- Joanne Turner, University of Liverpool
Please contact us if you are interested in our work.
Links
Managing Uncertainty in Complex Models: The MUCM project
Environment and Human Health Programme
[1] Codex Alimentarius Commission (1999). Principles And Guidelines For The Conduct Of Microbiological Risk Assessment.
[2] Miconnet N, Cornu M, Beaufort A, Rosso L and Denis JB (2005) Uncertainty distribution associated with estimating a proportion in microbial risk assessment. Risk Analysis 25 (1): 39-48.
[3] Powell M, Ebel E and Schlosser W (2001) Considering uncertainty in comparing the burden of illness due to foodborne microbial pathogens International Journal of Food Microbiology 69 (3): 209-215.
[4] Nauta MJ, Litman S, Barker GC and Carlin F (2003). A retail and consumer phase model for exposure assessment of Bacillus cereus. International Journal of Food Microbiology 83 (2) pp. 205-218.
[5] Kelly L, Smith, DL, Snary, EL, Johnson, JA, Harris, AD, Wooldridge, M and Morris, JG (2004). Animal growth promoters: to ban or not to ban? A risk assessment approach. International Journal Of Antimicrobial Agents 24 (3) pp. 205-212.
Related publications
Clough, H. E., Kennedy. M. C., Anderson, C. W., Barker, G., Cook, P., Hart, A., Hart, C. A., Malakar, P., Oakley, J. E., Pielaat, A., O’Hagan, A., Snary. E. L. and Turner, J.. Bayesian Approaches in Microbial Risk Assessment. In Proceedings of FoodMicro2008, Aberdeen, Scotland, 1-4th September 2008.
