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A Fuzzy Logic Inference Model for the Evaluation of the Effect of Extrinsic Factors on the Transmission of Infectious Diseases

Author

Listed:
  • Antonios Kalampakas

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Sovan Samanta

    (Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk 721636, West Bengal, India
    Research Center of Performance and Productivity Analysis, Istinye University, 34010 Istanbul, Türkiye)

  • Jayanta Bera

    (Department of Mathematics, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea)

  • Kinkar Chandra Das

    (Department of Mathematics, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea)

Abstract

COVID-19 is a contagious disease that poses a serious risk to public health worldwide. To reduce its spread, people need to adopt preventive behaviours such as wearing masks, maintaining physical distance, and isolating themselves if they are infected. However, the effectiveness of these measures may depend on various factors that differ across countries. This paper investigates how some factors, namely outsiders’ effect, life expectancy, population density, smoker percentage, and temperature, influence the transmission and death rate of COVID-19 in ninety-five top-affected countries. We collect and analyse the data of COVID-19 cases and deaths using statistical tests. We also use fuzzy logic to model the chances of COVID-19 based on the results of the statistical tests. Unlike the conventional uniform weighting of the rule base in fuzzy logic, we propose a novel method to calculate the weights of the rule base according to the significance of the factors. This study aims to provide a comprehensive and comparative analysis of the factors of COVID-19 transmission and death rates among different countries.

Suggested Citation

  • Antonios Kalampakas & Sovan Samanta & Jayanta Bera & Kinkar Chandra Das, 2024. "A Fuzzy Logic Inference Model for the Evaluation of the Effect of Extrinsic Factors on the Transmission of Infectious Diseases," Mathematics, MDPI, vol. 12(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:648-:d:1344181
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    References listed on IDEAS

    as
    1. Okposo, Newton I. & Adewole, Matthew O. & Okposo, Emamuzo N. & Ojarikre, Herietta I. & Abdullah, Farah A., 2021. "A mathematical study on a fractional COVID-19 transmission model within the framework of nonsingular and nonlocal kernel," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Kurubaran Ganasegeran & Mohd Fadzly Amar Jamil & Alan Swee Hock Ch’ng & Irene Looi & Kalaiarasu M. Peariasamy, 2021. "Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study," IJERPH, MDPI, vol. 18(18), pages 1-12, September.
    3. Felipe Carozzi & Sandro Provenzano & Sefi Roth, 2020. "Urban density and Covid-19," CEP Discussion Papers dp1711, Centre for Economic Performance, LSE.
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    Cited by:

    1. Antonios Kalampakas, 2025. "Fuzzy Graph Hyperoperations and Path-Based Algebraic Structures," Mathematics, MDPI, vol. 13(13), pages 1-32, July.

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