About: COVID-19 in Italy and extreme data mining

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 in Italy and extreme data mining
Creator	Grossi, Enzo Breda, Marco Massimo Buscema, Paolo Massini, Giulia Torre, Francesca
source	Elsevier; Medline; PMC
abstract	In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26(th), 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.
has issue date	2020-07-25(xsd:dateTime)
bibo:doi	10.1016/j.physa.2020.124991
bibo:pmid	32834435
has license	no-cc
sha1sum (hex)	7b31908c9d3ae7e90734bfed9caed9ee16dbcfe2
schema:url	https://doi.org/10.1016/j.physa.2020.124991
resource representing a document's title	COVID-19 in Italy and extreme data mining
has PubMed Central identifier	PMC7382358
has PubMed identifier	32834435
schema:publication	Physica A
resource representing a document's body	covid:7b31908c9d3ae7e90734bfed9caed9ee16dbcfe2#body_text
is schema:about of	named entity 'process' named entity 'Centroid' named entity 'Italy' named entity 'COVID-19' named entity 'PROCESS' »more»

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