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. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1002/sim.9004|
|Codice identificativo ISI:||WOS:000647472200001|
|Codice identificativo Scopus:||2-s2.0-85105234564|
|Titolo:||Nowcasting COVID-19 incidence indicators during the Italian first outbreak|
|Appare nelle tipologie:||1.1 Articolo in rivista|