Abstract
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.
Highlights
Different types of mathematical models are commonly used for pest risk analysis
Sensitivity analysis can be seen as an extension of uncertainty analysis
The uncertain quantities are allowed to vary within small intervals around nominal values, but these intervals are not related to the uncertainty ranges of the uncertain model inputs and parameters
Summary
Different types of mathematical models are commonly used for pest risk analysis. Some models are used for calculating probability of entry (e.g., Roberts et al 1998). It is difficult to define reliable probability distributions for all uncertain model inputs, i.e., probability distributions correctly reflecting the current state of knowledge about input values based on available data and expert knowledge In such cases, it is useful to define several probability distributions and, when possible, to run the analysis for all of them and to compare the results. It is useful to define several probability distributions and, when possible, to run the analysis for all of them and to compare the results This method is illustrated in the example below. The use of a small N value may lead to inaccurate estimated mean, variance, or quantiles because all of the space defined by the uncertain inputs or parameters may not be sampled, such that the resulting approximation of the probability distribution of the model output may be inaccurate. When several outputs are considered, it is often useful to study the relationship between different outputs using scatterplots and correlation coefficients
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