COVID-19 in Italy and extreme data mining

Paolo Massimo Buscema, Francesca Della Torre, Marco Breda, Giulia Massini and Enzo Grossi

Physica A: Statistical Mechanics and its Applications, 2020, vol. 557, issue C

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.

Keywords: Topological weighted centroid; COVID-19; Geographic profiling; Artificial intelligence; Epidemics; Adaptive systems (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120305173

DOI: 10.1016/j.physa.2020.124991

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