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Calibration of a SIR (Susceptibles-Infected-Recovered) model with officialinternational data for the COVID-19 pandemics provides a good example of thedifficulties inherent the solution of inverse problems. Inverse modeling is setup in a framework of discrete inverse problems, which explicitly considers therole and the relevance of data. Together with a physical vision of the model,the present work addresses numerically the issue of parameters calibration inSIR models, it discusses the uncertainties in the data provided byinternational authorities, how they influence the reliability of calibratedmodel parameters and, ultimately, of model predictions.

Inversion of a SIR-based model: a critical analysis about the application to COVID-19 epidemic