Nowcasting COVID-19 incidence indicators during the Italian first outbreak

Pierfrancesco Alaimo Di Loro; Fabio Divino; Alessio Farcomeni; Giovanna Jona Lasinio; Gianfranco Lovison; Antonello Maruotti; Marco Mingione

arXiv · 2010.12679

Nowcasting COVID-19 incidence indicators during the Italian first outbreak

Pierfrancesco Alaimo Di Loro·Fabio Divino·Alessio Farcomeni·Giovanna Jona Lasinio·Gianfranco Lovison·Antonello Maruotti·Marco Mingione

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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. Parameters 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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Pierfrancesco Alaimo Di Loro, Fabio Divino, Alessio Farcomeni, Giovanna Jona Lasinio, Gianfranco Lovison, Antonello Maruotti, Marco Mingione. 2020-10-23. Nowcasting COVID-19 incidence indicators during the Italian first outbreak. https://arxiv.org/abs/2010.12679

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