Identification of COVID-19 Spreaders Using Multiplex Networks Approach

In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.122874-122883
Main Authors: Montes-Orozco, Edwin, Mora-Gutierrez, Roman-Anselmo, De-Los-Cobos-Silva, Sergio-Gerardo, Rincon-Garcia, Eric-Alfredo, Torres-Cockrell, Gilberto-Sinuhe, Juarez-Gomez, Jorge, Obregon-Quintana, Bibiana, Lara-Velazquez, Pedro, Gutierrez-Andrade, Miguel-Angel
Format: Article
Language:English
Subjects:
Complex networks
complex systems
Computational and Artificial Intelligence
Computers and Information Processing
Coronaviruses
COVID-19
Economic analysis
Economic factors
Economics
Mathematical model
multiplex networks
Multiplexing
Networks
optimization
Pandemics
Separators
Social factors
social networks
Spreaders
Systems Engineering and Theory
Urban areas
Viruses
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Summary:In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3007726