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EFSA Panel on Biological Hazards (BIOHAZ); Scientific Opinion on Reflecting on the experiences and lessons learnt from modelling on biological hazards

机译:EFsa生物危害小组(BIOHaZ);关于反映生物危害模型经验教训的科学意见

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摘要

Quantitative analysis of scientific evidence involves the collection of data and modelling of a situation or process under consideration and this protocol is the basis of quantitative microbial risk assessments (QMRA). The lessons and experiences from quantitative risk assessments and modelling undertaken by the BIOHAZ Panel are reviewed. Quantitative models in risk assessments were found to be essential for providing an output that could be used by risk managers to support a proportionate response to a situation and/or to balance risks and costs. QMRA is a developing field which creates methodological uncertainties, and therefore, preferences for types of models cannot be specified. Newer approaches need to be identified and considered. Fit for purpose and simplicity are key issues when developing QMRA models. However, limits on time and resources may restrict the model selection. At the start, preferably before accepting the mandate, a scoping exercise is recommended. The scoping exercise could include an assessment of the mandate, possible interpretations of the terms of reference, deadlines, the modelling approaches possible and the data requirements. To support this process, a model catalogue could be developed. The choice of modelling approach is guided by the available data and cause-effect relationships. The basis/assumptions of each quantitative expression should be clearly stated as well as the associated uncertainties. Certain expressions such as “negligible”, “concern” and “unlikely” should be used carefully, with scientific criteria and context clearly defined, or avoided.
机译:科学证据的定量分析涉及数据的收集以及所考虑的情况或过程的建模,并且该协议是定量微生物风险评估(QMRA)的基础。回顾了BIOHAZ小组进行的定量风险评估和建模的经验教训。人们发现,风险评估中的定量模型对于提供可被风险管理者用来支持对情况的按比例反应和/或平衡风险和成本的输出至关重要。 QMRA是一个发展中的领域,它产生了方法上的不确定性,因此无法指定对模型类型的偏好。需要确定和考虑更新的方法。在开发QMRA模型时,满足目的和简化是关键问题。但是,时间和资源的限制可能会限制模型的选择。首先,最好是在接受授权之前,进行范围界定练习。范围界定活动可以包括对任务授权的评估,对职权范围的可能解释,期限,可能的建模方法以及数据要求。为了支持此过程,可以开发模型目录。建模方法的选择受可用数据和因果关系的指导。应当明确说明每个定量表达式的基础/假设以及相关的不确定性。应该谨慎使用某些表述,例如“微不足道”,“关注”和“不太可能”,并明确定义或避免使用科学标准和上下文。

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    Hald Tine;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 eng
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