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Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Author

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  • Patricia Melin

    (Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico)

  • Oscar Castillo

    (Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico)

Abstract

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Suggested Citation

  • Patricia Melin & Oscar Castillo, 2021. "Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8295-:d:600905
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    References listed on IDEAS

    as
    1. Melin, Patricia & Monica, Julio Cesar & Sanchez, Daniela & Castillo, Oscar, 2020. "Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Peichao Gao & Hong Zhang & Zhiwei Wu & Jicheng Wang, 2020. "Visualising the expansion and spread of coronavirus disease 2019 by cartograms," Environment and Planning A, , vol. 52(4), pages 698-701, June.
    3. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
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