This study proposes a three‐step procedure for the analysis of input–response relationships of dynamic models, which enables the analyst to develop a better understanding about the dynamics of the system. The main building block of the procedure is a random forest metamodel capturing the input–output relationships. We utilize an active learning approach as the second step to improve the accuracy of the metamodel. In the last step, we develop a novel way to present the information captured by the metamodel as a set of intelligible IF–THEN rules. For illustration, we use the FluTE model, which is an individual‐based influenza epidemic model. We observe that the number of daily applicable vaccines determines the success of an intervention strategy the most. Another critical observation is that when the daily available vaccines are constrained, nonpharmaceutical strategies should be incorporated to reduce the extent of the outbreak.

中文翻译：

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使用随机森林元模型和自适应序贯抽样分析基于个人的流感流行模型

这项研究提出了一个用于分析动态模型的输入-响应关系的三步过程，使分析人员可以更好地了解系统的动力学。该过程的主要构建块是捕获输入-输出关系的随机森林元模型。我们将主动学习方法用作提高元模型准确性的第二步。在最后一步中，我们开发了一种新颖的方法来将元模型捕获的信息呈现为一组可理解的IF–THEN规则。为了说明起见，我们使用FluTE模​​型，这是一个基于个人的流感流行模型。我们观察到，每日适用的疫苗数量决定了干预策略的成功最大。另一个重要的观察结果是，当每日可用的疫苗受到限制时，