Performing a Quantitative Microbial Risk Analysis Using Second-Order Monte Carlo Simulation (2009)
Quantitative microbial risk analysis modelling is increasingly being used in food safety as a tool to evaluate health risks. Accurately forming such models can be very difficult due to the uncertainty in the available data. Second order Monte Carlo simulation allows for the inclusion of this uncertainty and separates it from the variability incorporated in the model. This modelling process is illustrated by performing a simple risk assessment on the infection of Campylobacter during chicken preparation at a typical New Zealand barbecue.
CitationDawber, J. Brown, J.A., Horn, B. (2009) Performing a Quantitative Microbial Risk Analysis Using Second-Order Monte Carlo Simulation. Newcastle, Australia: 3rd Annual Applied Statistics, Education and Research Collaboration Conference, 7-8 Dec 2009. 4 pp.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsquantitative microbial risk analysis; campylobacter; variability; uncertainty
Showing items related by title, author, creator and subject.
Stochastic forest growth simulation: incorporating growth prediction uncertainty with wind and fire damage into carbon sequestration estimates and discounted cash flow analysis. Dowling, Leslie John (University of Canterbury. School of Forestry, 2015)Uncertainty in forest productivity prediction and the variable reduction from wind and fire damage makes predictions of forest Net Present Values (NPVs) and carbon sequestration uncertain, creating a distribution of possible ...
Performance of various BFGS and DFP implementations with limited precision second order information. Byatt, David (University of Canterbury, 2003)This paper supports the claim that there is no discernible advantage in choosing factorised implementations (over non–factorised implementations) of BFGS methods when approximate Hessian information is available to full ...
Monte Carlo Analysis of a Subcutaneous Absorption Insulin Glargine Model: Variability in Plasma Insulin Concentrations Razak, N.N.; Lin, J.; Wong, J.; Chase, Geoff (University of Canterbury. Mechanical Engineering, 2012)Absorption kinetics of long acting insulin such as Glargine often shows significant intra and inter-individual variability. To add this variability to the pharmacokinetics model of Glargine, ranges of variation for Glargine ...