Abstract
The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.
Highlights
The coronavirus disease 2019 (COVID-19) is a highly infectious disease associated with SARS-CoV-2 virus leading to a Severe Acute Respiratory Syndrome which has affected 22,683,769 confirmed patients and caused 793,773 deaths worldwide as of August 21, 2020 [1]
There is a huge literature on mathematical modeling in epidemiology starting from the early work of [2], going through the compartmental models of [3,4,5] laying down the foundations of modern epidemiology, up to the current data-driven approaches
This is done to see whether the different regional epidemic evolutions follow the usual pattern, whereby the initial stage of exponential growth is followed by a saturation stage
Summary
The coronavirus disease 2019 (COVID-19) is a highly infectious disease associated with SARS-CoV-2 virus leading to a Severe Acute Respiratory Syndrome which has affected 22,683,769 confirmed patients and caused 793,773 deaths worldwide as of August 21, 2020 [1]. Differently from most of previous studies on COVID-19 addressing large-scale, aggregate data on the disease spreading, the focus of the present paper is on the investigation of the multi-scale spreading across Italy, taking into account regional and national data from the database of the Italian Ministry of Health. We corroborate our findings, provided by the abovedescribed method, with hierarchical clustering and multidimensional analysis unfolding the similarities/ dissimilarities between the different regional scales in comparison with the national pandemic scale This data-driven strategy involves various processes, starting with a first phase dealing with data validation, sorting, summarization and aggregation, and moving to a more insightful phase involving the dynamical analysis, report, and classification. The core of the adopted schemes is the choice of the distance measure (see, e.g., [41]) and the implementation of some optimization indices for constructing graphical representations in terms of dendrograms and trees and multidimensional scaling (MDS) plots
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