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Pandemic Spreading in Italy and Regional Policies: An Approach with Self-organizing Maps

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Handbook of Artificial Intelligence in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 212))

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Abstract

The purpose of the chapter is using machine learning techniques (namely Self-Organizing Maps) to catch the emergence of clusters among Italian regions that can eventually contribute to explain the different behaviour of the pandemic within the same country. To do this, we have considered demographic, healthcare, and political data at regional level and we have tried going to the root of interactions among them. In this way, we obtained a model of the relations among variables with good explanatory capabilities, a kind of early-warning system which we hope could be helpful to address further intervention in the battle against COVID-19 pandemic.

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Author information

Authors and Affiliations

  1. School of Social Sciences, Department of Economics, University of Genova, via Vivaldi 5, 16126, Genova, Italy

    Marina Resta

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  1. Marina Resta
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Corresponding author

Correspondence to Marina Resta .

Editor information

Editors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia

    Chee-Peng Lim

  2. College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

    Yen-Wei Chen

  3. Royal Adelaide Hospital, Adelaide, SA, Australia

    Ashlesha Vaidya

  4. Avanti Institute of Cardiology, Nagpur, India

    Charu Mahorkar

  5. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Resta, M. (2022). Pandemic Spreading in Italy and Regional Policies: An Approach with Self-organizing Maps. In: Lim, CP., Chen, YW., Vaidya, A., Mahorkar, C., Jain, L.C. (eds) Handbook of Artificial Intelligence in Healthcare. Intelligent Systems Reference Library, vol 212. Springer, Cham. https://doi.org/10.1007/978-3-030-83620-7_8

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