Author: Alaimo Di Loro, Pierfrancesco; Divino, Fabio; Farcomeni, Alessio; Jona Lasinio, Giovanna; Lovison, Gianfranco; Maruotti, Antonello; Mingione, Marco
Title: Nowcasting COVIDâ€19 incidence indicators during the Italian first outbreak Cord-id: o3tmv09v Document date: 2021_5_6
ID: o3tmv09v
Snippet: A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by realâ€time monitoring and shortâ€term forecasting of the main epidemiological indicators within the first outbreak of COVIDâ€19 in Italy. Accurate shortâ€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),
Document: A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by realâ€time monitoring and shortâ€term forecasting of the main epidemiological indicators within the first outbreak of COVIDâ€19 in Italy. Accurate shortâ€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.
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