Nowcasting COVID-19 incidence indicators during the Italian first outbreak

TL;DR

This paper introduces a new parametric regression model for real-time COVID-19 incidence data, enabling accurate short-term forecasts and better resource allocation during Italy's first outbreak.

Contribution

It presents a novel regression approach that incorporates external variables for improved epidemic nowcasting and forecasting accuracy.

Findings

Accurate short-term predictions of COVID-19 incidence in Italy.

Effective estimation of epidemic peak time and height.

Reproducible computational framework provided.

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.