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A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
Mathematical Biosciences ( IF 4.3 ) Pub Date : 2020-11-17 , DOI: 10.1016/j.mbs.2020.108514
Chiara Piazzola , Lorenzo Tamellini , Raúl Tempone

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.



中文翻译:

关于类SIR流行病学动态系统不确定性和可识别性下的预测工具的说明

我们概述了可用于不确定性和动态系统数据拟合下的预测方法,以及在此情况下出现的基本挑战。重点放在类似SIR的模型上,这些模型通常在尝试预测COVID-19大流行趋势时使用。特别是,我们提出了一个关于SIR类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,从而很难将这些模型用于有意义的预测。实际上,我们接触的大多数点通常在更一般的设置中对反问题有效。

更新日期:2020-11-17
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