Nowcasting COVID‐19 incidence indicators during the Italian first outbreak. (6th May 2021)

• Record Type: Journal Article
• Authors: Alaimo Di Loro, Pierfrancesco; Divino, Fabio; Farcomeni, Alessio; Jona Lasinio, Giovanna; Lovison, Gianfranco; Maruotti, Antonello; Mingione, Marco
• Is Part Of: Statistics in medicine. Volume 40:Number 16(2021)
• Page Start: 3843
• Page End: 3864
• Publication Date: 2021-05-06
• Subjects: COVID‐19 -- growth curves -- Richards' equation -- SARS‐CoV‐2; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9004
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : 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.