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A fractal kinetics SI model can explain the dynamics of COVID-19 epidemics

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  • Kosmas Kosmidis
  • Panos Macheras

Abstract

The COVID-19 pandemic has already had a shocking impact on the lives of everybody on the planet. Here, we present a modification of the classical SI model, the Fractal Kinetics SI model which is in excellent agreement with the disease outbreak data available from the World Health Organization. The fractal kinetic approach that we propose here originates from chemical kinetics and has successfully been used in the past to describe reaction dynamics when imperfect mixing and segregation of the reactants is important and affects the dynamics of the reaction. The model introduces a novel epidemiological parameter, the “fractal” exponent h which is introduced in order to account for the self-organization of the societies against the pandemic through social distancing, lockdowns and flight restrictions.

Suggested Citation

  • Kosmas Kosmidis & Panos Macheras, 2020. "A fractal kinetics SI model can explain the dynamics of COVID-19 epidemics," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-9, August.
    • Handle: RePEc:plo:pone00:0237304
    DOI: 10.1371/journal.pone.0237304
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    Cited by:

    1. Chénangnon Frédéric Tovissodé & Bruno Enagnon Lokonon & Romain Glèlè Kakaï, 2020. "On the use of growth models to understand epidemic outbreaks with application to COVID-19 data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    2. Ilyass Dahmouni & Elnaz Kanani Kuchesfehani, 2022. "Necessity of Social Distancing in Pandemic Control: A Dynamic Game Theory Approach," Dynamic Games and Applications, Springer, vol. 12(1), pages 237-257, March.
    3. Yiannis Contoyiannis & Stavros G. Stavrinides & Michael P. Hanias & Myron Kampitakis & Pericles Papadopoulos & Rodrigo Picos & Stelios M. Potirakis, 2020. "A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    4. Shanshan Wan & Zhuo Chen & Cheng Lyu & Ruofan Li & Yuntao Yue & Ying Liu, 2022. "Research on disaster information dissemination based on social sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    5. Javier Cifuentes-Faura & Ursula Faura-Martínez & Matilde Lafuente-Lechuga, 2022. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    6. Tong Wang & Yang Liu & Qiyuan Li & Peng Du & Xiaogong Zheng & Qingfei Gao, 2023. "State-of-the-Art Review of the Resilience of Urban Bridge Networks," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    7. Rana Yousif & Aref Jeribi & Saad Al-Azzawi, 2023. "Fractional-Order SEIRD Model for Global COVID-19 Outbreak," Mathematics, MDPI, vol. 11(4), pages 1-19, February.

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