论文标题
基于SIR模型的反转：关于COVID-19的应用的批判性分析
Inversion of a SIR-based model: a critical analysis about the application to COVID-19 epidemic
论文作者
论文摘要
使用官方国际数据进行COVID-19-PANDEMICS的SIR（受感染的恢复）模型的校准，提供了一个很好的例子，说明了固有的逆问题解决方案的困难。反向建模是在离散反问题框架内建立的，该框架明确考虑了数据的角色和相关性。与模型的物理视野一起，目前的工作从数值上解决了SIR模型中的参数校准问题，它讨论了国际当局提供的数据中的不确定性，它们如何影响校准的模型参数的可靠性，并最终影响模型预测。
Calibration of a SIR (Susceptibles-Infected-Recovered) model with official international data for the COVID-19 pandemics provides a good example of the difficulties inherent the solution of inverse problems. Inverse modeling is set up in a framework of discrete inverse problems, which explicitly considers the role and the relevance of data. Together with a physical vision of the model, the present work addresses numerically the issue of parameters calibration in SIR models, it discusses the uncertainties in the data provided by international authorities, how they influence the reliability of calibrated model parameters and, ultimately, of model predictions.
