  A novel parametric regression model is proposed to fit incidence data
typically collected during epidemics. The proposal is motivated by real-time
monitoring and short-term forecasting of the main epidemiological indicators
within the first outbreak of COVID-19 in Italy. Accurate short-term
predictions, including the potential effect of exogenous or external variables
are provided; this ensures to accurately predict important characteristics of
the epidemic (e.g., peak time and height), allowing for a better allocation of
health resources over time. Parameters estimation is carried out in a maximum
likelihood framework. All computational details required to reproduce the
approach and replicate the results are provided.
