Uncertainty and sensitivity analysis in quantitative pest risk assessments : practical rules for risk assessors

  • Quantitative models have several advantages compared to qualitative methods for pest risk assessments (PRA). Quantitative models do not require the definition of categorical ratings and can be used to compute numerical probabilities of entry and establishment, and to quantify spread and impact. These models are powerful tools, but they include several sources of uncertainty that need to be taken into account by risk assessors and communicated to decision makers. Uncertainty analysis (UA) and sensitivity analysis (SA) are useful for analyzing uncertainty in models used in PRA, and are becoming more popular. However, these techniques should be applied with caution because several factors may influence their results. In this paper, a brief overview of methods of UA and SA are given. As well, a series of practical rules are defined that can be followed by risk assessors to improve the reliability of UA and SA results. These rules are illustrated in a case study based on the infection model of Magarey et al. (2005) where the results of UA and SA are shown to be highly dependent on the assumptions made on the probability distribution of the model inputs.

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Metadaten
Author:David Makowski
URN:urn:nbn:de:hebis:30:3-323837
DOI:https://doi.org/10.3897/neobiota.18.3993
Parent Title (English):NeoBiota
Document Type:Article
Language:English
Date of Publication (online):2013/11/21
Date of first Publication:2013/09/13
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2013/11/21
Tag:Model; Pest risk assessment; Sensitivity; Uncertainty
Issue:18
Page Number:15
First Page:157
Last Page:171
HeBIS-PPN:363177019
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Sammlung Biologie / Sondersammelgebiets-Volltexte
Zeitschriften / Jahresberichte:NeoBiota / NeoBiota 18
:urn:nbn:de:hebis:30:3-321124
Licence (German):License LogoCreative Commons - Namensnennung 3.0