A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy

TL;DR

This paper develops a predictive model using generalized additive models to forecast emergency call volumes and events during COVID-19 in Lombardy, Italy, aiding emergency response planning amid pandemic-related fluctuations.

Contribution

It introduces a novel predictive model employing GAM with negative binomial distribution to accurately forecast emergency events during COVID-19, accounting for seasonal and weekly variations.

Findings

Model achieves reasonable mean absolute errors during 2020-2021

Variables like day of the week significantly affect call volume

Model captures changes in call patterns during COVID-19 period

Abstract

Italy, particularly the Lombardy region, was among the first countries outside of Asia to report cases of COVID-19. The emergency medical service called Regional Emergency Agency (AREU) coordinates the intra- and inter-regional non-hospital emergency network and the European emergency number service in Lombardy. AREU must deal with daily and seasonal variations of call volume. The number and type of emergency calls changed dramatically during the COVID-19 pandemic. A model to predict incoming calls and how many of these turn into events, i.e., dispatch of transport and equipment until the rescue is completed, was developed to address the emergency period. We used the generalized additive model with a negative binomial family to predict the number of events one, two, five, and seven days ahead. The over-dispersion of the data was tackled by using the negative binomial family and the nonlinear relationship between the number of events and covariates (e.g., seasonal effects) by smoothing splines. The model coefficients show the effect of variables, e.g., the day of the week, on the number of events and how these effects change during the pre-COVID-19 period. The proposed model returns reasonable mean absolute errors for most of the 2020-2021 period.