  The coronavirus disease 2019 (COVID-19) has changed the world since the World
Health Organization declared its outbreak on 30th January 2020, recognizing the
outbreak as a pandemic on 11th March 2020. As often said by politicians and
scientific advisors, the objective is "to flatten the curve", or "push the peak
down", or similar wording, of the virus spreading. Central to the official
advice are mathematical models and data, which provide estimates on the
evolution of the number of infected, recovered and deaths. The accuracy of the
models is improved day by day by inferring the contact, recovery, and death
rates from data (confirmed cases). A data-driven model trained with {\it both}
data {\it and} first principles is proposed. The model can quickly be
re-trained any time that new data becomes available. The method can be applied
to more detailed epidemic models with virtually no conceptual modification.
