期刊
NONLINEAR DYNAMICS
卷 101, 期 3, 页码 1583-1619出版社
SPRINGER
DOI: 10.1007/s11071-020-05902-1
关键词
COVID-19; Compartmental model; Logistic regression; Nonlinear infection dynamics; Parametric identification; Computational intelligence
资金
- Universita degli Studi Roma Tre within the CRUI-CARE Agreement
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.
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