Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy
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ORIGINAL PAPER
Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy Giuseppe Quaranta · Giovanni Formica · J. Tenreiro Machado · Walter Lacarbonara Sami F. Masri
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Received: 3 July 2020 / Accepted: 17 August 2020 © The Author(s) 2020
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 G. Quaranta · W. Lacarbonara Department of Structural and Geotechnical Engineering, Sapienza University of Rome, via Eudossiana 18, Rome, Italy e-mail: [email protected] W. Lacarbonara e-mail: [email protected] G. Formica (B) Department of Architecture, University of Rome Tre, via Madonna dei Monti 40, Rome, Italy e-mail: [email protected] J. T. Machado Department of Electrical Engineering, Institute of Engineering, Polytechnic of Port, Rua Dr. Antònio Bernardino de Almeida, 431, 4249-015 Porto, Portugal e-mail: [email protected] S. F. Masri Department of Civil Engineering, University of Southern California, 3620 S. Vermont Ave, KAP 210, MC 2531, Los Angeles, CA 90089-2531, USA e-mail: [email protected]
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. Keywords COVID-19 · Compartmental model · Logistic regression · Nonlinear infection dynamics · Parametric identification · Computational intelligence
1 Introduction 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]. After the officially reported outbreak in China in December 2019, COVID-19 has spread across the globe, at a faster rate than expected, to the level of a global pandemic causing health emergencies, huge economic losses, and social instabilities worldwide. Governments have been faced with new challenges such as quick enforcement of severe control measures (case isolation, social distancing, travel restrictions, and quarantine of local or national magnitude) to slow down the virus spreading and prevent a collapse of their
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healthcare systems, which would have caused a sign
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