Nowcasting COVID-19 incidence indicators during the Italian first outbreak
Applications
2020-10-27 v1
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.
Keywords
Cite
@article{arxiv.2010.12679,
title = {Nowcasting COVID-19 incidence indicators during the Italian first outbreak},
author = {Pierfrancesco Alaimo Di Loro and Fabio Divino and Alessio Farcomeni and Giovanna Jona Lasinio and Gianfranco Lovison and Antonello Maruotti and Marco Mingione},
journal= {arXiv preprint arXiv:2010.12679},
year = {2020}
}