论文标题
对由流行病学促进的连续时间马尔可夫链模型进行全局灵敏度分析
Performing global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology
论文作者
论文摘要
在本文中，我们在化学反应网络框架中应用了纳瓦罗·希门尼斯（Navarro Jimenez）等（2016）中引入的方法，以对由流行病学动机的连续时间马尔可夫链模型进行模拟进行全球灵敏度分析。我们的目标不仅是量化不确定参数的影响，例如流行参数（传输速率，平均隔离时间），还量化了固有随机性和固有随机性和固有随机性之间的相互作用的效果。为此，遵循纳瓦罗·希门尼斯（Navarro Jimenez）等人提出的内容，我们利用了三种精确的模拟算法，用于连续时间马尔可夫链中的最新动物状态，这些算法与Sobol（1993）中介绍的基于方差的灵敏度分析的常见工具相结合。此外，我们讨论了用于仿真的仿真算法选择对灵敏度分析结果的影响。至少据我们所知，这样的讨论是新的。在数值部分中，我们基于从SARS-COV-2流行模型的不同精确仿真算法获得的模拟实施和比较三个灵敏度分析。
In this paper we apply a methodology introduced in Navarro Jimenez et al (2016) in the framework of chemical reaction networks to perform a global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology. Our goal is to quantify not only the effects of uncertain parameters such as epidemic parameters (transmission rate, mean sojourn duration in compartments), but also those of intrinsic randomness and interactions between epidemic parameters and intrinsic randomness. For that purpose, following what was proposed in Navarro Jimenez et al, we leverage three exact simulation algorithms for continuous-time Markov chains from the state of the art which we combine with common tools from variance-based sensitivity analysis as introduced in Sobol (1993). Also, we discuss the impact of the choice of the simulation algorithm used for the simulations on the results of sensitivity analysis. Such a discussion is new, at least to our knowledge. In a numerical section, we implement and compare three sensitivity analyses based on simulations obtained from different exact simulation algorithms of a SARS-CoV-2 epidemic model.
