  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.
