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The Complex Systems for Conflict Interaction Modelling to Describe a Non-Trivial Epidemiological Situation

Author

Listed:
  • Svajone Bekesiene

    (General Jonas Zemaitis Military Academy of Lithuania, Research Group on Logistics and Defence Technology Management, Silo 5a, 10322 Vilnius, Lithuania)

  • Igor Samoilenko

    (Department of Operations Research, Taras Shevchenko National University of Kyiv, UA-03680 Kyiv, Ukraine)

  • Anatolij Nikitin

    (Department of Applied Research, The National University of Ostroh Academy, UA-03580 Ostroh, Ukraine
    Faculty of Natural Sciences, Jan Kochanowski University, Stefana Żeromskiego 5, 25-369 Kielce, Poland)

  • Ieva Meidute-Kavaliauskiene

    (General Jonas Zemaitis Military Academy of Lithuania, Research Group on Logistics and Defence Technology Management, Silo 5a, 10322 Vilnius, Lithuania)

Abstract

This study investigates a complex system that describes a non-trivial epidemiological model with integrated internal conflict (interregional migration) on the example of cyclic migration using the software. JetBrains PyCharm Community Edition 2020.3.3, a free and open-source integrated development environment (IDE) in the Python programming language, was chosen as the software development tool. The Matplotlib 3.5 library was used to display the modelling results graphically. The integration of internal conflict into the model revealed significant and notable changes in its behavior. This study’s results prove that not only the characteristics of the interaction factors but also the size of the values determine the direction of migration concerning relation to competitors.

Suggested Citation

  • Svajone Bekesiene & Igor Samoilenko & Anatolij Nikitin & Ieva Meidute-Kavaliauskiene, 2022. "The Complex Systems for Conflict Interaction Modelling to Describe a Non-Trivial Epidemiological Situation," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:537-:d:745279
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    References listed on IDEAS

    as
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    3. Tuan, Nguyen Huy & Mohammadi, Hakimeh & Rezapour, Shahram, 2020. "A mathematical model for COVID-19 transmission by using the Caputo fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Jahanshahi, Hadi & Munoz-Pacheco, Jesus M. & Bekiros, Stelios & Alotaibi, Naif D., 2021. "A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Taiwo Olubunmi Sangodapo & Babatunde Oluwaseun Onasanya & Sarka Mayerova-Hoskova, 2021. "Decision-Making with Fuzzy Soft Matrix Using a Revised Method: A Case of Medical Diagnosis of Diseases," Mathematics, MDPI, vol. 9(18), pages 1-12, September.
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    Cited by:

    1. Alena Vagaská, 2023. "Mathematical–Statistical Nonlinear Model of Zincing Process and Strategy for Determining the Optimal Process Conditions," Mathematics, MDPI, vol. 11(3), pages 1-21, February.
    2. Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.

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