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Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps

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  • Melin, Patricia
  • Monica, Julio Cesar
  • Sanchez, Daniela
  • Castillo, Oscar

Abstract

We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained, that could be helpful in deciding the best strategies in dealing with this virus. Most of the previous papers dealing with data of the Coronavirus have viewed the problem on temporal aspect, which is also important, but this is mainly concerned with the forecast of the numeric information. However, we believe that the spatial aspect is also important, so in this view the main contribution of this paper is the use of unsupervised self-organizing maps for grouping together similar countries in their fight against the Coronavirus pandemic, and thus proposing that strategies for similar countries could be established accordingly.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303179
    DOI: 10.1016/j.chaos.2020.109917
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(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.
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    Cited by:

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    8. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    9. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Mohammad Tabasi & Ali Asghar Alesheikh & Elnaz Babaie & Javad Hatamiafkoueieh, 2022. "Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    11. 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.
    12. Castillo, Oscar & Melin, Patricia, 2020. "Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    13. Diego Galvan & Luciane Effting & Hágata Cremasco & Carlos Adam Conte-Junior, 2020. "Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    14. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    15. Castillo, Oscar & Melin, Patricia, 2021. "A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).

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