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 in 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.

使用 COVID-19 大流行的官方国际数据校准 SIR（易感物 - 感染 - 恢复）模型提供了一个很好的例子，说明了逆问题解决方案中固有的困难。逆建模是在离散逆问题的框架中建立的，该框架明确考虑了数据的作用和相关性。连同模型的物理视觉，目前的工作以数值方式解决了 SIR 模型中参数校准的问题，它讨论了国际权威机构提供的数据的不确定性，它们如何影响校准模型参数的可靠性，并最终影响模型的可靠性。预测。