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10:07
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
Published on: January 31, 2020
Second-order modeling of variability and uncertainty in microbial hazard characterization
Andrea S Vicari1, Amirhossein Mokhtari, Roberta A Morales
1Department of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina 27605-8401, USA.
Journal of Food Protection
|March 8, 2007
Summary
This study introduces a framework for quantifying variability and uncertainty in microbial hazard characterization. Uncertainty in dose-response modeling significantly impacts outcomes more than factors like age.
Area of Science:
- Microbiology
- Risk Assessment
- Quantitative Analysis
Background:
- Microbial hazard characterization requires quantitative assessment of variability and uncertainty.
- Existing models may not fully integrate these factors, impacting risk assessment accuracy.
Purpose of the Study:
- To develop and apply an analytical framework for quantitative consideration of variability and uncertainty in microbial hazard characterization.
- To assess the impact of uncertainty and variability on microbial dose-response modeling.
Main Methods:
- Employed second-order modeling with two-dimensional Monte Carlo simulation.
- Utilized the bootstrap method to simulate sampling error in dose-response modeling.
- Analyzed human feeding trials with Campylobacter jejuni and FoodNet surveillance data.
Main Results:
- Uncertainty in dose-response modeling was found to be the dominant factor influencing analytical outcomes.
- The impact of age on Campylobacter susceptibility showed limited influence on the overall assessment.
- The framework successfully integrated uncertainty and variability into the hazard characterization process.
Conclusions:
- Characterizing key sources of uncertainty and their propagation is crucial for microbial risk assessment.
- While modeling variability is important, addressing uncertainty is of greater immediate significance.
- The developed framework provides a robust approach for enhanced microbial risk assessment.
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