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Novel class of susceptible–infectious–recovered models involving power-law interactions

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  • Kaniadakis, G.

Abstract

It is shown that the ordinary SIR (Susceptible–Infectious–Recovered) epidemic model exhibits features that are common to a class of compartmental models with power-law interactions. Within this class of theoretical models, the standard SIR model emerges as a singular non-integrable model. Various integrable models, whose solutions are defined explicitly or implicitly in terms of elementary functions, are discovered within the same class. A Hamiltonian dynamics with position-depending forces underlies a sub-class of these models. The general class of models is very flexible and capable of describing epidemics characterized by a finite or indefinite lifespan. In the last case, the compartment population distributions evolve in time exhibiting exponential or power-law tails.

Suggested Citation

  • Kaniadakis, G., 2024. "Novel class of susceptible–infectious–recovered models involving power-law interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
  • Handle: RePEc:eee:phsmap:v:633:y:2024:i:c:s0378437123009925
    DOI: 10.1016/j.physa.2023.129437
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    References listed on IDEAS

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    1. Turkyilmazoglu, Mustafa, 2022. "A restricted epidemic SIR model with elementary solutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Lazebnik, Teddy, 2023. "Computational applications of extended SIR models: A review focused on airborne pandemics," Ecological Modelling, Elsevier, vol. 483(C).
    3. Bettencourt, Luís M.A. & Cintrón-Arias, Ariel & Kaiser, David I. & Castillo-Chávez, Carlos, 2006. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 513-536.
    4. Lewi Stone & Ronen Olinky & Amit Huppert, 2007. "Seasonal dynamics of recurrent epidemics," Nature, Nature, vol. 446(7135), pages 533-536, March.
    5. Sene, Ndolane, 2020. "SIR epidemic model with Mittag–Leffler fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    6. Satsuma, J & Willox, R & Ramani, A & Grammaticos, B & Carstea, A.S, 2004. "Extending the SIR epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 369-375.
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