Incorporating model uncertainties along with data uncertainties in microbial risk assessment

Seung Ho Kang, Ralph L. Kodell, James J. Chen

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

Much research on food safety has been conducted since the National Food Safety Initiative of 1997. Risk assessment plays an important role in food safety practices and programs, and various dose-response models for estimating microbial risks have been investigated. Several dose-response models can provide reasonably good fits to the data in the experimental dose range, but yield risk estimates that differ by orders of magnitude in the low-dose range. Hence, model uncertainty can be just important as data uncertainty (experimental variation) in risk assessment. Although it is common in risk assessment to account for data uncertainty, it is uncommon to account for model uncertainties. In this paper we incorporate data uncertainties with confidence limits and model uncertainties with a weighted average of an estimate from each of various models. A numerical tool to compute the maximum likelihood estimates and confidence limits is addressed. The proposed method for incorporating model uncertainties is illustrated with real data sets. (C) 2000 Academic Press.

Original languageEnglish
Pages (from-to)68-72
Number of pages5
JournalRegulatory Toxicology and Pharmacology
Volume32
Issue number1
DOIs
Publication statusPublished - 2000 Jan 1

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Risk assessment
Uncertainty
Food safety
Food Safety
Likelihood Functions
Maximum likelihood
Research

All Science Journal Classification (ASJC) codes

  • Toxicology

Cite this

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title = "Incorporating model uncertainties along with data uncertainties in microbial risk assessment",
abstract = "Much research on food safety has been conducted since the National Food Safety Initiative of 1997. Risk assessment plays an important role in food safety practices and programs, and various dose-response models for estimating microbial risks have been investigated. Several dose-response models can provide reasonably good fits to the data in the experimental dose range, but yield risk estimates that differ by orders of magnitude in the low-dose range. Hence, model uncertainty can be just important as data uncertainty (experimental variation) in risk assessment. Although it is common in risk assessment to account for data uncertainty, it is uncommon to account for model uncertainties. In this paper we incorporate data uncertainties with confidence limits and model uncertainties with a weighted average of an estimate from each of various models. A numerical tool to compute the maximum likelihood estimates and confidence limits is addressed. The proposed method for incorporating model uncertainties is illustrated with real data sets. (C) 2000 Academic Press.",
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Incorporating model uncertainties along with data uncertainties in microbial risk assessment. / Kang, Seung Ho; Kodell, Ralph L.; Chen, James J.

In: Regulatory Toxicology and Pharmacology, Vol. 32, No. 1, 01.01.2000, p. 68-72.

Research output: Contribution to journalArticle

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