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Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

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.

SUBMITTER: Alaimo Di Loro P

PROVIDER: S-EPMC8242495 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

Alaimo Di Loro Pierfrancesco P Divino Fabio F Farcomeni Alessio A Jona Lasinio Giovanna G Lovison Gianfranco G Maruotti Antonello A Mingione Marco M

Statistics in medicine 20210506 16

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 ...[more]

PMID: 33955571

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