Uncertainty assessment using the NUSAP approach: a case study on the EFoNAO tool
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Abstract
Many of the current policy issues that have to be addressed are suffering from large uncertainties, while decision stakes are high. Addressing the uncertainties explicitly in risk assessments becomes especially important when addressing such issues. This report describes the evaluation of an uncertainty typology combined with a NUSAP (acronym for Numeral, Unit, Spread, Pedigree and Assessment) approach. The uncertainty typology is used for uncertainty characterization based on six dimensions, aiding the choice for subsequent approaches to dealing with the uncertainties in e.g. decision making. One such approach is the NUSAP method, which is used for prioritizing uncertainty sources based on the scientific strength and influence on results as judged by experts. The case study selected was EFSA’s Food of Non-Animal origin risk ranking model, which aims to identify and rank pathogen and food combinations based on public health concern. Sixteen uncertainty sources were identified and characterized with the uncertainty typology. The 16 uncertainty sources were implicit or explicit assumptions and assessed as such for scientific strength and influence on results during a NUSAP workshop. Criteria for assessing the scientific strength included: influence of situational limitation, assumption plausibility, choice space and peer agreement. Overall, the combination of uncertainty typology and NUSAP was found to be very helpful as method for uncertainty assessment, as the procedure increases insight in uncertainty sources related to model outcomes and in their impact on the end result. The attention to uncertainty was recommended to be integrated in risk assessments, as it aids to formalise discussions about uncertainties. It was also recommended that practicality and feasibility aspects should be considered when incorporating uncertainty assessment in such assessments, that a clear and consistent terminology should be used and that training of experts is required to integrate elements of uncertainty assessment in future risk assessment activities