  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.
