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COVID-19 in Italy and extreme data mining

Author

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

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.

Suggested Citation

  • Buscema, Paolo Massimo & Della Torre, Francesca & Breda, Marco & Massini, Giulia & Grossi, Enzo, 2020. "COVID-19 in Italy and extreme data mining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    • Handle: RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120305173
    DOI: 10.1016/j.physa.2020.124991
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    References listed on IDEAS

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    1. Buscema, Massimo & Sacco, Pier Luigi & Massini, Giulia & Della Torre, Francesca & Brogi, Marco & Salonia, Massimo & Ferilli, Guido, 2018. "Unraveling the space grammar of terrorist attacks: A TWC approach," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 230-254.
    2. Buscema, Massimo & Massini, Giulia & Sacco, Pier Luigi, 2018. "The Topological Weighted Centroid (TWC): A topological approach to the time-space structure of epidemic and pseudo-epidemic processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 582-627.
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    Citations

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    Cited by:

    1. Buscema, Massimo & Ferilli, Guido & Gustafsson, Christer & Massini, Giulia & Sacco, Pier Luigi, 2022. "A nonlinear, data-driven, ANNs-based approach to culture-led development policies in rural areas: The case of Gjakove and Peć districts, Western Kosovo," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Pascoal, R. & Rocha, H., 2022. "Population density impact on COVID-19 mortality rate: A multifractal analysis using French data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

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    2. Buscema, Massimo & Ferilli, Guido & Gustafsson, Christer & Massini, Giulia & Sacco, Pier Luigi, 2022. "A nonlinear, data-driven, ANNs-based approach to culture-led development policies in rural areas: The case of Gjakove and Peć districts, Western Kosovo," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
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