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Strain-stream model of epidemic spread in application to COVID-19

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  • S. A. Trigger

    (Joint Institute for High Temperatures, Russian Academy of Sciences
    Humboldt-Universität zu Berlin)

  • A. M. Ignatov

    (Prokhorov General Physics Institute of the Russian Academy of Sciences)

Abstract

The recently developed model of the epidemic spread of two virus strains in a closed population is generalized to the situation typical for the couple of strains delta and omicron, when there is a high probability of omicron infection soon enough after recovering from delta infection. This model can be considered as a kind of combination of SIR and SIS models for the case of competition of two strains of the same virus with different contagiousness in a population. The obtained equations and results can be directly implemented for practical calculations of the replacement of strains of the SARS-CoV-2 virus. A comparison between the estimated replacement time and the corresponding statistics shows reasonable agreement. Graphic abstract

Suggested Citation

  • S. A. Trigger & A. M. Ignatov, 2022. "Strain-stream model of epidemic spread in application to COVID-19," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(11), pages 1-8, November.
    • Handle: RePEc:spr:eurphb:v:95:y:2022:i:11:d:10.1140_epjb_s10051-022-00457-z
    DOI: 10.1140/epjb/s10051-022-00457-z
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    References listed on IDEAS

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    1. Xiao-Jie Li & Xiang Li, 2020. "Vaccinating SIS epidemics under evolving perception in heterogeneous networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(10), pages 1-7, October.
    2. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    3. Juliet Bedford & Jeremy Farrar & Chikwe Ihekweazu & Gagandeep Kang & Marion Koopmans & John Nkengasong, 2019. "A new twenty-first century science for effective epidemic response," Nature, Nature, vol. 575(7781), pages 130-136, November.
    4. Simón A. Rella & Yuliya A. Kulikova & Emmanouil T. Dermitzakis & Fyodor A. Kondrashov, 2021. "Rates of SARS-COV-2 transmission and vaccination impact the fate of vaccine-resistant strains," Working Papers 2129, Banco de España.
    5. Khadija Khan & Farina Karim & Sandile Cele & Kajal Reedoy & James Emmanuel San & Gila Lustig & Houriiyah Tegally & Yuval Rosenberg & Mallory Bernstein & Zesuliwe Jule & Yashica Ganga & Nokuthula Ngcob, 2022. "Omicron infection enhances Delta antibody immunity in vaccinated persons," Nature, Nature, vol. 607(7918), pages 356-359, July.
    6. Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    Cited by:

    1. Shnip, A.I. & Trigger, S.A., 2024. "On the repeated epidemic waves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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