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.
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.
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak / ALAIMO DI LORO, Pierfrancesco; Divino, Fabio; Farcomeni, Alessio; JONA LASINIO, Giovanna; Lovison, Gianfranco; Maruotti, Antonello; Mingione, Marco. - In: STATISTICS IN MEDICINE. - ISSN 1097-0258. - (2021), pp. 1-22. [10.1002/sim.9004]
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
Pierfrancesco Alaimo Di Loro
;Fabio Divino;Alessio Farcomeni;Giovanna Jona Lasinio;Antonello Maruotti;Marco Mingione
2021
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.| File | Dimensione | Formato | |
|---|---|---|---|
|
Alaimo Di Loro_Nowcasting_2021.pdf
accesso aperto
Note: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.9004
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Creative commons
Dimensione
1.81 MB
Formato
Adobe PDF
|
1.81 MB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
