Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities

Author:

Gianquintieri Lorenzo,Brovelli Maria AntoniaORCID,Pagliosa Andrea,Dassi Gabriele,Brambilla Piero Maria,Bonora Rodolfo,Sechi Giuseppe Maria,Caiani Enrico GianlucaORCID

Abstract

The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; hence, it is now known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.

Publisher

MDPI AG

Subject

Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development

Reference44 articles.

1. Istituto Superiore di Sanità, Roma–Aggiornamento Nazionale 09 Marzo 2020 https://www.ansa.it/documents/1583864041148_Bollettino.pdf

2. Jon Hopkins University of Medicine–Coronavirus Research Center–Global Map https://coronavirus.jhu.edu/map.html

3. Artificial intelligence and machine learning to fight COVID-19

4. How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

5. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

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