  The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7
million deaths, and put the world's most advanced healthcare systems under
heavy stress. In many countries, drastic restrictive measures adopted by
political authorities, such as national lockdowns, have not prevented the
outbreak of new pandemic's waves. In this article, we propose an integrated
detection-estimation-forecasting framework that, using publicly available data,
is designed to: (i) learn relevant features of the pandemic (e.g., the
infection rate); (ii) detect as quickly as possible the onset (or the
termination) of an exponential growth of the contagion; and (iii) reliably
forecast the pandemic evolution. The proposed solution is validated by
analyzing the COVID-19 second and third waves in the USA.
