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Updated: Nov 4, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Spatial robust fuzzy clustering of COVID 19 time series based on B-splines
Pierpaolo D'Urso1, Livia De Giovanni2, Vincenzina Vitale1
1Department of Social end Economic Sciences, Sapienza University of Rome, P.za Aldo Moro, 5 00185 Rome, Italy.
Abstract:
The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.
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