Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model
Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model
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DOI：
10.1137/16m1093008
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发表时间：
2017-01-01
影响因子：
2
通讯作者：
White, Richard G.
中科院分区：
文献类型：
--
作者：
Andrianakis, Loannis;McCreesh, Nicky;White, Richard G.
历史匹配是一种模型（预）校准方法，已广泛应用于各种科学学科的计算机模型。在这项工作中，我们将历史匹配应用于基于个人的艾滋病毒流行病学模型，该模型具有96个输入参数和50个输出参数，该模型的规模比使用此方法或类似方法之前校准的其他模型大得多。除了证明历史匹配可以分析这种复杂性的模型之外，这项工作的一个核心贡献是使用线性回归进行历史匹配，这是一种比以前使用的基于高斯过程的模拟器更基本且更容易实现的统计工具。此外，我们通过引入适应该方法特定需求的采样算法，解决了历史匹配的实际困难，即对微小的、非难以置信的空间进行采样。本文提出的历史匹配方法的有效性和简单性表明，它是一种有用的工具，用于校准计算昂贵的、高维的、基于个体的模型。
History matching is a model (pre-)calibration method that has been applied to computer models from a wide range of scientific disciplines. In this work we apply history matching to an individual-based epidemiological model of HIV that has 96 input and 50 output parameters, a model of much larger scale than others that have been calibrated before using this or similar methods. Apart from demonstrating that history matching can analyze models of this complexity, a central contribution of this work is that the history match is carried out using linear regression, a statistical tool that is elementary and easier to implement than the Gaussian process-based emulators that have previously been used. Furthermore, we address a practical difficulty with history matching, namely, the sampling of tiny, nonimplausible spaces, by introducing a sampling algorithm adjusted to the specific needs of this method. The effectiveness and simplicity of the history matching method presented here shows that it is a useful tool for the calibration of computationally expensive, high dimensional, individual-based models.