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Hierarchical Bayesian analysis of censored microbiological contamination data for use in risk assessment and mitigation

机译：审查微生物污染数据的分级贝叶斯分析，用于风险评估和缓解

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摘要

Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributions that summarize contamination data for use in quantitative microbiological risk assessment, especially when variability and uncertainty are to be characterized separately. In this paper, distributions are fit using Bayesian analysis, and results are compared to results obtained with a methodology based on maximum likelihood estimation and the non-parametric bootstrap method. The Bayesian model is also extended hierarchically to estimate the effects of the individual elements of a covariate such as, for example, on a national level, the food processing company where the analyzed food samples were processed, or, on an international level, the geographical origin of contamination data. Including this extra information allows a risk assessor to differentiate between several scenario's and increase the specificity of the estimate of risk of illness, or compare different scenario's to each other. Furthermore, inference is made on the predictive importance of several different covariates while taking into account uncertainty, allowing to indicate which covariates are influential factors determining contamination.
机译：微生物污染数据通常由于没有检测到或由于已知测量结果仅小于，大于或介于实验室程序规定的某些边界值之间而受到检查。因此，要拟合出汇总污染数据以用于定量微生物风险评估的分布并不容易，特别是当变异性和不确定性要分别表征时。在本文中，使用贝叶斯分析对分布进行拟合，并将结果与​​使用基于最大似然估计和非参数自举法的方法获得的结果进行比较。贝叶斯模型也进行了层次扩展，以估计协变量的各个元素的影响，例如，在国家一级，处理所分析食品样本的食品加工公司，或者在国际一级，地理区域污染数据的来源。包含这些额外的信息可以使风险评估人员在几种方案之间进行区分，并提高疾病风险估计的特异性，或者将不同方案之间进行比较。此外，在考虑不确定性的同时对几个不同协变量的预测重要性进行了推断，从而可以指出哪些协变量是确定污染的影响因素。

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  • 来源
    《Food microbiology》 |2011年第4期|p.712-719|共8页
  • 作者

    P. Busschaert; A.H. Geeraerd; M. Uyttendaele; J.F. Van Impe;

  • 作者单位

    CPMF2-Flemish Cluster Predictive Microbiology in Foods, Belgium,Division of Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering,Katholieke Universiteit Leuven. W. de Croylaan 46, B-3001 Leuven, Belgium;

    CPMF2-Flemish Cluster Predictive Microbiology in Foods, Belgium,Division of Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems (BIOSYST), Katholieke Universiteit Leuven. W. de Croylaan 42, B-3001 Leuven, Belgium;

    Laboratory of Food Microbiology and Food Preservation, Department of Food Safety and Food Quality, Chent University, Coupure Links 653, B-9000 Ghent, Belgium;

    CPMF2-Flemish Cluster Predictive Microbiology in Foods, Belgium,Division of Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering,Katholieke Universiteit Leuven. W. de Croylaan 46, B-3001 Leuven, Belgium;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Quantitative microbiological risk; Assessment; uncertainty; Variability; Hierarchical bayesian modeling;

    机译：定量微生物风险;评估;不确定度;变异性;层次贝叶斯模型;

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