Evaluation and recommendation of sensitivity analysis methods for application...

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Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models
评价和推荐敏感性分析方法应用于随机人体暴露和剂量模拟模型

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research tore duce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect tot heir applicability tohuman exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency’s Stochastic Human Exposure and Dose Simulation (SHEDS) model. The
methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol’s method. The first five methods are known as ‘‘sampling-based’’ techniques, wheras the latter two methods are known as ‘‘variancebased’’ techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol’s method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol’s method
and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the samplingbased methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.
      敏感性分析暴露或风险模型可以帮助识别最重要的因素，以帮助风险管理或优先考虑额外的研究，以减少估计的不确定性。然而，灵敏度分析受到非线性、输入之间的交互作用和多日或时间尺度的挑战。选择的敏感性分析方法是评估其适用性的人类暴露模型与这样的特征使用一个试验台。该试验台是美国环境保护局的人体随机暴露与剂量模拟(SHEDS)模型的简化版。评价方法包括Pearson和Spearman相关、样本和秩回归、方差分析、傅立叶振幅灵敏度检验(FAST)和Sobol方法。前五种方法被称为基于抽样的技术，而后两种方法被称为基于方差的技术。测试用例的主要目标是识别单个输入对输出方差的主要和总贡献。Sobol的方法和FAST直接量化了这些测量的灵敏度。结果表明，当在不同的时间尺度下评估时，输入的灵敏度通常会发生变化(例如，每天与每月)。所有方法都对不那么重要的投入提供了类似的见解;然而，与基于采样的技术相比，Sobol方法和FAST在重要输入的灵敏度方面提供了更稳健的洞见。因此，基于抽样的方法可以在筛选步骤中使用，以识别不重要的输入，然后应用更计算密集型的精炼方法，以较小的输入集合。对风险管理的敏感性结果的时间变化的含义进行了简要的讨论。

Keywords: SHEDS models, sensitivity analysis, variance-based methods, sampling-based methods, riskasse ssment.
关键词:流模型，敏感性分析，基于方差的方法，基于抽样的方法，风险分析。