论文标题
在不确定性和类似SIR样动力学系统的不确定性和可识别性下的预测工具的注释
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
论文作者
论文摘要
我们概述了在动态系统的不确定性和数据拟合以及在这种情况下出现的基本挑战的方法中可用于预测的方法。重点是类似SIR样模型，这些模型通常在尝试预测Covid-19大流行的趋势时通常使用。特别是，我们提出了有关SIR样模型参数可识别性的警告标志；通常，即使对于非常简单的模型，也可能很难从数据中推断出参数的正确值，从而使使用这些模型进行有意义的预测变得不平凡。实际上，我们涉及的大多数要点通常对于更通用的设置中的反问题有效。
We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.
