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Open AccessArticle

An Analysis of the Italian Lockdown in Retrospective Using Particle Swarm Optimization in Machine Learning Applied to an Epidemiological Model

by 1,2
1
IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy
2
TREE-TOWER s.r.l., Via della Chiesa XXXII trav. I n. 231, 55100 Lucca, Italy
Physics 2020, 2(3), 368-382; https://doi.org/10.3390/physics2030020
Received: 2 July 2020 / Revised: 27 July 2020 / Accepted: 29 July 2020 / Published: 1 August 2020
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
A critical analysis of the open data provided by the Italian Civil Protection Centre during phase 1 of Covid-19 epidemic—the so-called Italian lockdown—is herein proposed in relation to four of the most affected Italian regions, namely Lombardy, Reggio Emilia, Valle d’Aosta, and Veneto. A possible bias in the data induced by the extent in the use of medical swabs is found in relation to Valle d’Aosta and Veneto. Observed data are then interpreted using a Susceptible-Infectious-Recovered (SIR) epidemiological model enhanced with asymptomatic (infected and recovered) compartments, including lockdown effects through time-dependent model parameters. The initial number of susceptible individuals for each region is also considered as a parameter to be identified. The issue of parameters identification is herein addressed by a robust machine learning approach based on particle swarm optimization. Model predictions provide relevant information for policymakers in terms of the effect of lockdown measures in the different regions. The number of susceptible individuals involved in the epidemic, important for a safe release of lockdown during the next phases, is predicted to be around 10% of the population for Lombardy, 16% for Reggio Emilia, 18% for Veneto, and 40% for Valle d’Aosta. View Full-Text
Keywords: nonlinear collective dynamics; machine learning; particle swarm optimization; epidemiological models; healthcare policies nonlinear collective dynamics; machine learning; particle swarm optimization; epidemiological models; healthcare policies
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MDPI and ACS Style

Paggi, M. An Analysis of the Italian Lockdown in Retrospective Using Particle Swarm Optimization in Machine Learning Applied to an Epidemiological Model. Physics 2020, 2, 368-382. https://doi.org/10.3390/physics2030020

AMA Style

Paggi M. An Analysis of the Italian Lockdown in Retrospective Using Particle Swarm Optimization in Machine Learning Applied to an Epidemiological Model. Physics. 2020; 2(3):368-382. https://doi.org/10.3390/physics2030020

Chicago/Turabian Style

Paggi, Marco. 2020. "An Analysis of the Italian Lockdown in Retrospective Using Particle Swarm Optimization in Machine Learning Applied to an Epidemiological Model" Physics 2, no. 3: 368-382. https://doi.org/10.3390/physics2030020

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