Predicting the Impact of COVID-19 on the Emergency Departments in Lombardy, Italy

Abstract

Italy, and in particular the Lombardy region, was among the first countries outside of Asia to report cases of COVID19. The Lombardy region relies on the emergency medical service called Agenzia Regionale Emergenza Urgenza (AREU). It coordinates the intra and inter regional non hospital emergency network and the European emergency number service. Therefore, AREU must deal with daily and seasonal variations of call volume. Many factors can describe this call volume across time beyond the annual trend, such as weather circumstances and epidemiological factors. Factors related to the day of the week, time of the day, seasonal and yearly variations that characterize the time series pattern must also be considered. In addition, the number and type of calls to the emergency call center changed dramatically during and after the COVID19 epidemic peak. Statistical modeling is essential for AREU to predict incoming calls and how many of these turn into events, i.e., dispatch of transport and equipment until the rescue is completed. In this talk, we present the Generalized Additive Model that we proposed to AREU to predict the number of events during the COVID19 pandemic.

Date
Aug 8, 2021 12:00 AM
Location
Seattle, USA
Seattle,
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Angela Andreella
Assistant Professor at Ca’ Foscari University of Venice

My research interests include Multiple Testing problem and Procrustes technique, generally statistical methods in the Neuroscience field.