  We propose a robust parameter estimation method for dynamical systems based
on Statistical Learning techniques which aims to estimate a set of parameters
that well fit the dynamics in order to obtain robust evidences about the
qualitative behaviour of its trajectory. The method is quite general and
flexible, since it does not rely on any specific property of the dynamical
system, and represents a reinterpretation of Approximate Bayesian Computation
methods through the lens of Statistical Learning. The method is specially
useful for estimating parameters in epidemiological compartmental models in
order to obtain qualitative properties of a disease evolution. We apply it to
simulated and real data about COVID-19 spread in the US in order to evaluate
qualitatively its evolution over time, showing how one may assess the
effectiveness of measures implemented to slow the spread and some qualitative
features of the disease current and future evolution.
