Uncertainty and Variability in Risk Assessment

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20 record(s) found




ID Author(s) Title / Description Publication Year
9382 Kodell, R.; Gaylor, D.

Combining Uncertainty Factors in Deriving Human Exposure Levels of Noncarcinogenic Toxicants
Combining Uncertainty Factors in Deriving Human Exposure Levels of Noncarcinogenic Toxicants
URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1749-6632.1999.tb08085.x/full

This paper shows how estimates of means and standard deviations of the approximately log-normal distributions of individual uncertainty factors can be used to estimate percentiles of the distribution of…

This paper shows how estimates of means and standard deviations of the approximately log-normal distributions of individual uncertainty factors can be used to estimate percentiles of the distribution of the product of uncertainty factors. 

Acceptable levels of human exposure to noncarcinogenic toxicants in environmental and occupational settings generally are derived by reducing experimental no-observed-adverse-effect levels (NOAELs) or benchmark doses (BDs) by a product of uncertainty factors. The common default value for each uncertainty factor is 10. The author illustrates that an appropriately selected upper percentile, for example, 95th or 99th, of the distribution of the product can be used as a combined uncertainty factor to replace the conventional product of default factors.

 

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Source: Annals of the New York Academy of Sciences, Vol 895, Dec. 1999, p. 188-195

Keywords:

exposure, methodology, pollutants, risk assessment, toxic substances, uncertainty

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19991200
1999
9381 Renwick, A.G.

use of safety or uncertainty factors in the setting of acute reference doses
The use of safety or uncertainty factors in the setting of acute reference doses
URL: http://www.informaworld.com/smpp/89560457-17988228/content~db=all~content=a713810707~tab=content~order=page

This article provides a rationale for the application of uncertainty factors for chronic exposure and illustrates the analysis of the proportion of the population covered by the 10-fold factor…

This article provides a rationale for the application of uncertainty factors for chronic exposure and illustrates the analysis of the proportion of the population covered by the 10-fold factor for human variability, analysis of special groups (e.g., infants and children, ethnic differences, genetic polymorphisms), analysis of the consequences of intakes in excess of the ADI, TDI or RfD, critical effect and NOAEL and modification of uncertainty factors for determination of acute reference dose values.

This article explains that the establishment of an acute reference dose based on animal studies has to allow for both species differences and inter-individual variability; comparison with the factors used for chronic effects suggests that modification of the usual defaults may be appropriate under certain specific circumstances, but that the usual default of 100 remains appropriate for most cases.

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Source: Food Additives & Contaminants. Vol. 17, No. 7, Jul. 2000, p. 627 - 635.

Keywords:

dose response, exposure, methodology, pharmacokinetics, risk assessment, uncertainty, variability

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20000700
2000
9380 Thompson, K. M.

Variability and Uncertainty Meet Risk Management and Risk Communication
Variability and Uncertainty Meet Risk Management and Risk Communication
URL: http://onlinelibrary.wiley.com/doi/10.1111/0272-4332.00044/abstract

This article uses the case studies of variability in the risks of dying on the ground from a crashing airplane and from the deployment of motor vehicle airbags to…

This article uses the case studies of variability in the risks of dying on the ground from a crashing airplane and from the deployment of motor vehicle airbags to demonstrate how better characterization of variability and uncertainty in the risk assessment lead to better risk communication. Analogies to food safety and environmental risks are also discussed. This presentation demonstrates that probabilistic risk assessment has an impact on both risk management and risk communication, and highlights remaining research issues associated with using improved sensitivity and uncertainty analyses in risk assessment.

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Source: Risk Analysis, Vol. 22, No. 3, Jun. 2002, p. 647–654.

Keywords:

methodology, quantitative risk assessment, risk assessment, risk communication, risk management, statistical analysis, uncertainty, variability

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20020600
2002
9379 Hart, A.; Smith, G.; Macarthur, R.; Rose, M.

Application of uncertainty analysis in assessing dietary exposure
Application of uncertainty analysis in assessing dietary exposure
URL: http://dx.doi.org/10.1016/S0378-4274(03)00040-7

This paper reviews examples of the application of different methods to the assessment of dietary exposure to food contaminants, including dioxins in seafood, where it was found that the…

This paper reviews examples of the application of different methods to the assessment of dietary exposure to food contaminants, including dioxins in seafood, where it was found that the greatest uncertainties relate to toxicity rather than exposure. This article provides an overview of conventional approaches for assessing dietary exposure to contaminants and additives in food. It also provides examples of uncertainty analysis in exposure assessment and also covers uncertainty and the limits of detection. Further work required to implement probabilistic approaches for dietary exposure assessment is also discussed.

 

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Source: Toxicology Letters, Vol. 140-141, Apr. 2003, p. 437-442

Keywords:

detection limit, dietary exposure, dioxins, food additives, food contamination, polychlorinated biphenyls, toxicity, uncertainty

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20030400
2003
9378 Mokhtari, A.; Frey, H.C.

Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance
Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance
URL: http://www.ncbi.nlm.nih.gov/pubmed/16506979

This article demonstrates the application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR)…

This article demonstrates the application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error.  In this article, sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.

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Source: Risk Analysis, Vol. 25, No. 6, Dec. 2005, p. 1511-29.

Keywords:

methodology, models, quantitative risk assessment, risk assessment, statistical analysis, uncertainty, variability

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20051200
2005
9377 Borgonovo, E.

Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches
Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches
URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2006.00806.x/abstract

This article describes uncertainty importance measures as quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Uncertainty importance application ranges from food safety to hurricane…

This article describes uncertainty importance measures as quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Uncertainty importance application ranges from food safety to hurricane losses. Results and indications an analyst derives depend on the method selected for the study. By means of an example the author shows that output variance does not always reflect a decisionmaker state of knowledge of the inputs. In this article, numerical results demonstrate that both moment-independent and variance-based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.

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Source: Risk Analysis, Vol. 26, No. 5, Oct. 2006, p. 1349-1361

Keywords:

methodology, quantitative analysis, quantitative risk assessment, risk assessment, statistical analysis, uncertainty

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20061000
2006
9363 Bailar, J.C. 3rd, Bailer, A.J.

Risk assessment--the mother of all uncertainties. Disciplinary perspectives on uncertainty in risk assessment
Risk assessment--the mother of all uncertainties. Disciplinary perspectives on uncertainty in risk assessment
URL: http://www.ncbi.nlm.nih.gov/pubmed/10676423

This article illustrates that uncertainty in the detection and evaluation of chemical hazards to health leads to challenges when conducting risk assessments. Some of the uncertainty has to do…

This article illustrates that uncertainty in the detection and evaluation of chemical hazards to health leads to challenges when conducting risk assessments. Some of the uncertainty has to do with data, some with incomplete understanding of processes, and some with the most fundamental ways of viewing the questions.

True variability--across space, in time, or among individuals--complicates the search for understanding many important aspects of risk. A few statistical and toxicologic tools are available to assess uncertainty. This article briefly discusses three methods of classifying uncertainty. 

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Source: Annals of the New York Academy of Sciences, 895, 1999, p. 273-85.

Keywords:

chemical hazards, methodology, risk assessment, statistics, toxicology, uncertainty, variability

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19990000
1999
9344 Pouillot, R.; Albert, I.; Cornu, M.; Denis, J.

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes
Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes
URL: http://dx.doi.org/10.1016/S0168-1605(02)00192-7

This article articulates the usefulness of modeling and evaluating risk uncertainty and variability separately. This paper proposes a Bayesian procedure for growth parameter estimation which makes it possible to…

This article articulates the usefulness of modeling and evaluating risk uncertainty and variability separately.  This paper proposes a Bayesian procedure for growth parameter estimation which makes it possible to separate these two components by means of hyperparameters.

This model incorporates in a single step the logistic equation with delay as a primary growth model and the cardinal temperature equation as a secondary growth model.

The estimation of Listeria monocytogenes growth parameters in milk using literature data is proposed as a detailed application.

While this model should be applied on genuine data, it is highlighted that the proposed approach may be convenient for estimating the variability and uncertainty of growth parameters separately, using a complete predictive microbiology model.

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Source: International Journal of Food Microbiology, Vol. 81, No. 2, March 2003, p. 87-104.

Keywords:

Bayesian theory, Listeria monocytogenes, methodology, microbiological risk assessment, milk, models, predictive microbiology, risk assessment, uncertainty, variability

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20030315
2003
9320 Havelaar, A.; Nauta, M.

“Second-Order Modeling of Variability and Uncertainty in Microbial Hazard Characterization,” A Comment on: J. Food Prot. 70(2):363-372 (2007)
“Second-Order Modeling of Variability and Uncertainty in Microbial Hazard Characterization,” A Comment on: J. Food Prot. 70(2):363-372 (2007)
URL: http://www.ingentaconnect.com/content/iafp/jfp/2007/00000070/00000002/art00013

The authors of this letter agreed with Vicari et al. that second order modeling is highly recommended in microbial risk assessment, but disagree with the choice of dose-response model…

The authors of this letter agreed with Vicari et al. that second order modeling is highly recommended in microbial risk assessment, but disagree with the choice of dose-response model by Vicari et al. They also pointed out that, in contrast to what is stated in the introduction, this approach has already been demonstrated in the literature.

 

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Source: Journal of Food Protection, Vol. 70, No. 10, Oct. 2007, p. 2228-2229

Keywords:

Campylobacter jejuni, exposure assessment, hazard characterization, methodology, microbiological risk assessment, models, risk assessment, uncertainty, variability

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20071000
2007
9319 Vicari, A., et al

Second-Order Modeling of Variability and Uncertainty in Microbial Hazard Characterization
Second-Order Modeling of Variability and Uncertainty in Microbial Hazard Characterization
URL: http://www.ingentaconnect.com/content/iafp/jfp/2007/00000070/00000002/art00013

This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into…

This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into homogeneous population subgroups was applied to integrate uncertainty and variability.  

The results indicate that uncertainty associated with dose-response modeling has a dominating influence on the analytical outcome. In contrast, inclusion of the age factor has a limited impact. While the advocacy of more closely modeling variability in hazard characterization is warranted, the characterization of key sources of uncertainties and their consistent propagation throughout a microbial risk assessment actually appear of greater importance.

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Source: Journal of Food Protection

Keywords:

biological hazards, Campylobacter jejuni, dose response, hazard characterization, methodology, microbiological risk assessment, models, Monte Carlo method, risk assessment, uncertainty, variability

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20070200
2007
9318 Delignette-Muller, M.L.; Rosso, L.

Biological Variability and Exposure Assessment
Biological Variability and Exposure Assessment
URL: http://www.sciencedirect.com/science/article/B6T7K-40PXM01-8/2/a8d8321b87a6da2b0b7739039dfb4620

Predictive models are now commonly used for exposure assessment, with growth parameters defined for each microbial species. In this study, we tried to take into account microbial growth variability…

Predictive models are now commonly used for exposure assessment, with growth parameters defined for each microbial species. In this study, we tried to take into account microbial growth variability among strains of a single species. Bacillus cereus in pasteurized milk was chosen to illustrate the influence of the biological variability on the outcome of exposure assessment. Each parameter of the exposure assessment (growth parameters, shelf-life conditions) was characterized by a probability distribution describing variability and/or uncertainty. The impact of the intra-species variability on the result of the exposure assessment was then quantified and discussed. Two simple domestic shelf life conditions were tested. The results confirm that the biological variability has a great impact on the accuracy of the result and should not be systematically neglected.

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Source: International Journal of Food Microbiology

Keywords:

exposure assessment, microbiological risk assessment, predictive microbiology, uncertainty, variability

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20000715
2000
9140 Nauta, M.J.

Separation of uncertainty and variability in quantitative microbial risk assessment models
Separation of uncertainty and variability in quantitative microbial risk assessment models
URL: http://dx.doi.org/10.1016/S0168-1605(00)00225-7

Quantitative risk assessment (QRA) second-order modeling, involving the separation of uncertainty and variability of model parameters, is considered of increasing importance in several fields of risk analysis. In this…

Quantitative risk assessment (QRA) second-order modeling, involving the separation of uncertainty and variability of model parameters, is considered of increasing importance in several fields of risk analysis. In this paper the relevance of second-order modeling in microbial risk assessment is illustrated by a simple example of a risk assessment of growth of B. cereus in pasteurised milk. It shows that the prediction of the outbreak size may depend on the way that uncertainty and variability are separated, and that a major outbreak may be overlooked if the distinction between uncertainty and variability is neglected.

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Source: Government of Netherlands, National Institute of Public Health and the Environment (RIVM), Microbiological Laboratory for Health Protection

Keywords:

Bacillus cereus, disease outbreaks, methodology, milk, models, pasteurization, quantitative risk assessment, risk assessment, uncertainty, variability

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20000610
2000
9078 Pouillot, R.; Delignette-Muller, M.L.

Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages
Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages
URL: http://dx.doi.org/10.1016/j.ijfoodmicro.2010.07.011

This article introduces the use of two new quantitative risk assessment computing resources (R packages) specifically developed to help risk assessors in their projects. The first package, fitdistrplus, gathers…

This article introduces the use of two new quantitative risk assessment computing resources (R packages) specifically developed to help risk assessors in their projects. The first package, “fitdistrplus”, gathers tools for choosing and fitting a parametric univariate distribution to a given dataset. The second package, “mc2d”, helps to build and study two dimensional (or second-order) Monte-Carlo simulations in which the estimation of variability and uncertainty in the risk estimates is separated. The usefulness of these packages is illustrated through a risk assessment of hemolytic and uremic syndrome in children linked to the presence of Escherichia coli O157:H7 in ground beef. These R packages are freely available at the Comprehensive R Archive Network (cran.r-project.org).

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Source: International Journal of Food Microbiology, Vol. 142, No. 3, Sep. 1, 2010, p. 330-340.

Keywords:

beef, Escherichia coli O157:H7, ground beef, hemolytic uremic syndrome, methodology, Monte Carlo method, quantitative risk assessment, risk assessment, uncertainty, variability

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20100901
2010
8963 Miconnet, N., et al

Uncertainty Distribution Associated with Estimating a Proportion in Microbial Risk Assessment
Uncertainty Distribution Associated with Estimating a Proportion in Microbial Risk Assessment
URL: http://onlinelibrary.wiley.com/doi/10.1111/j.0272-4332.2005.00565.x/pdf

In this study, the sampling uncertainty associated with estimating a low proportion on the basis of a small sample size was considered.

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Source: Risk Analysis, Vol. 25, No. 1, Feb. 2005, p. 39-48

Keywords:

bacteria, Bayesian theory, biological hazards, fish, fish products, food contamination, Listeria, Listeria monocytogenes, methodology, microbiological risk assessment, models, Monte Carlo method, quantitative analysis, quantitative risk assessment, risk assessment, seafoods, shellfish, simulation models, uncertainty

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20050200
2005
7797 European Food Safety Authority

Guidance of the Scientific Committee on a request from EFSA related to Uncertainties in Dietary Exposure Assessment
Guidance of the Scientific Committee on a request from EFSA related to Uncertainties in Dietary Exposure Assessment
URL: http://www.efsa.europa.eu/EFSA/Scientific_Opinion/sc_sum_uncertainty%20exp_en.pdf?ssbinary=true

This opinion addresses the issue of scientific uncertainties in dietary exposure assessment.

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Source: The European Food Safety Autority Journal v. 438, p. 1-54.

Keywords:

dietary exposure, exposure assessment, methodology, risk assessment, uncertainty

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20061214
2006
7770 World Health Organization, International Programme on Chemical Safety

Uncertainty and Data Quality in Exposure Assessment
Uncertainty and Data Quality in Exposure Assessment
URL: http://www.who.int/ipcs/publications/methods/harmonization/exposure_assessment.pdf

This guidance has been developed as a basis for transparently characterizing uncertainty in chemical exposure assessment to enable its full consideration in regulatory and policy decision-making processes.

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Source: World Health Organization, International Programme on Chemical Safety

Keywords:

exposure assessment, methodology, risk assessment, uncertainty

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20080000
2008
2639 Kang, S.; Kodell, R.L.; Chen, J.J.

Incorporating Model Uncertainties Along With Data Uncertainties in Microbial Risk Assessment
Incorporating Model Uncertainties Along With Data Uncertainties in Microbial Risk Assessment
URL: http://dx.doi.org/10.1006/rtph.2000.1404

This paper discusses the importance of considering model uncertainty in risk assessment and proposes a method for incorporating model uncertainties into uncertainty estimates. 

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Source: Regulatory Toxicology and Pharmacology, Vol. 32, Issue 1, Aug. 2000, p. 68-72/ScienceDirect

Keywords:

biological hazards, methodology, microbiological risk assessment, models, risk assessment, uncertainty

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20000800
2000
2124 Zheng, J., Frey, H.C.

AuvTool
AuvTool
URL: http://foodrisk.org/exclusives/AuvTool/

This software tool, developed for the Office of Research and Development, Environmental Protection Agency, is for use in quantifying variability and uncertainty in quantitative analysis. The tool was presented…

This software tool, developed for the Office of Research and Development, Environmental Protection Agency, is for use in quantifying variability and uncertainty in quantitative analysis. The tool was presented at the Society for Risk Analysis Annual Meeting in New Orleans, LA, in a workshop entitled "Bootstrap Simulation and Two- Dimensional Monte Carlo Simulation: Dealing with Variability and Uncertainty, Mixture Distributions, Measurement Error, and Censored Data." The software is available to download, as is a user's manual, technical documentation, workshop handouts and slides, and a paper entitled "Quantification of variability and uncertainty using mixture distributions: evaluation of sample size, mixing weights and separation between components"

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Source: FoodRisk.org

Keywords:

computer software, exposure, methodology, probability distribution, risk assessment, simulation models, statistical models

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20020200
2002
1608 Hammonds, J.S.; Hoffmanm, F.O.; Bartell, S.M.

Introductory Guide to Uncertainty Analysis in Environmental and Health Risk Assessment
Introductory Guide to Uncertainty Analysis in Environmental and Health Risk Assessment
URL: http://rais.ornl.gov/documents/tm35r1.pdf

Report on methods for evaluating uncertainty in risk assessment mathematical equations and computer models. Includes discussion of analytical and numerical methods for error propogation and of methods for identifying…

Report on methods for evaluating uncertainty in risk assessment mathematical equations and computer models. Includes discussion of analytical and numerical methods for error propogation and of methods for identifying the most important contributors to uncertainty

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Source: Risk Assessment Program, Toxicology and Risk Analysis Section, Life Sciences Division, Oak Ridge National Laboratory, Department of Energy

Keywords:

methodology, risk analysis, risk assessment, uncertainty

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19941200
1994
1247 Herrman, J.L.; Nakashima, N.

Assuring Science-Based Decisions: Expert Advice and Risk Analysis - Validity of the Process and Dealing with Uncertainty
Assuring Science-Based Decisions: Expert Advice and Risk Analysis - Validity of the Process and Dealing with Uncertainty
URL: http://www.fao.org/docrep/meeting/X2621E.htm

This report was presented at the Conference on International Food Trade Beyond 2000: Science-Based Decisions, Harmonization, Equivalence, and Mutual Recognition, in Melbourne, Australia, October 11-15, 1999. It discusses the…

This report was presented at the Conference on International Food Trade Beyond 2000: Science-Based Decisions, Harmonization, Equivalence, and Mutual Recognition, in Melbourne, Australia, October 11-15, 1999. It discusses the importance of scientific advice in the risk analysis process, and includes suggestions on strengthening that process

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Source: Food and Agriculture Organization of the United Nations

Keywords:

Australia, decision making, expert opinion, food transport, risk analysis, trade, uncertainty, validity

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19991011
1999