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Counter Intituive COVID-19 Propagation Dynamics in Brazil

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Hybrid Artificial Intelligent Systems (HAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12886))

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Abstract

There are few studies concerning the propagation of COVID-19 pandemic, besides theoretical models that have produced alarming predictions. These models assume wave-like propagation dynamics on an uniform medium. However, it appears that COVID-19 follows unexpected propagation dynamics. In this paper we consider the state-wise data on COVID-19 mortality in Brazil provided by government sources. Conventional propagation models tell us that the pandemic should propagate over the neighboring states from the initial cases in Sao Paulo. We compute several measures of correlation and prediction by random forests finding that the patterns of propagation do not correlate well with the graph distances defined by the spatial neighborhood of the states. We think that this phenomenon deserves further attention in order to understand COVID-19 pandemic.

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Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country UPV/EHU, Leioa, Spain

    Manuel Graña, Goizalde Badiola-Zabala & Jose Manuel Lopez-Guede

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  1. Manuel Graña
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  2. Goizalde Badiola-Zabala
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  3. Jose Manuel Lopez-Guede
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Correspondence to Manuel Graña .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Graña, M., Badiola-Zabala, G., Lopez-Guede, J.M. (2021). Counter Intituive COVID-19 Propagation Dynamics in Brazil. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_36

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  • DOI: https://doi.org/10.1007/978-3-030-86271-8_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86270-1

  • Online ISBN: 978-3-030-86271-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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