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Algorithm for Preventing the Spread of COVID-19 in Airports and Air Routes by Applying Fuzzy Logic and a Markov Chain

Author

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  • Cesar Guevara

    (Centre of Mechatronics and Interactive Systems (MIST), Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito 170103, Ecuador
    The Institute of Mathematical Sciences (ICMAT), DataLab, C/Nicolás Cabrera, 13-15, 28049 Madrid, Spain)

  • Diego Bonilla

    (Centre of Mechatronics and Interactive Systems (MIST), Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito 170103, Ecuador)

Abstract

Since the start of COVID-19 and its growth into an uncontrollable pandemic, the spread of diseases through airports has become a serious health problem around the world. This study presents an algorithm to determine the risk of spread in airports and air routes. Graphs are applied to model the air transport network and Dijkstra’s algorithm is used for generating routes. Fuzzy logic is applied to evaluate multiple demographics, health, and transport variables and identify the level of spread in each airport. The algorithm applies a Markov chain to determine the probability of the arrival of an infected passenger with the COVID-19 virus to an airport in any country in the world. The results show the optimal performance of the proposed algorithm. In addition, some data are presented that allow for the application of actions in health and mobility policies to prevent the spread of infectious diseases.

Suggested Citation

  • Cesar Guevara & Diego Bonilla, 2021. "Algorithm for Preventing the Spread of COVID-19 in Airports and Air Routes by Applying Fuzzy Logic and a Markov Chain," Mathematics, MDPI, vol. 9(23), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3040-:d:689074
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    References listed on IDEAS

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