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Research Article | Open Access

Exact solutions and superposition rules for Hamiltonian systems generalizing time-dependent SIS epidemic models with stochastic fluctuations

Rutwig Campoamor-Stursberg1,2Eduardo Fernández-Saiz3Francisco J. Herranz4( )
Interdisciplinary Mathematical Institute (IMI), Complutense University, Madrid 28040, Spain
Departament of Algebra, Geometry and Topology, Faculty of Mathematics, Complutense University, Madrid 28040, Spain
Department of Mathematics and Data Science, San Pablo-CEU University, Alcorcón 28925, Spain
Departament of Physics, University of Burgos, Burgos 09001, Spain
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Abstract

Using the theory of Lie-Hamilton systems, formal generalized time-dependent Hamiltonian systems that extend a recently proposed SIS epidemic model with a variable infection rate are considered. It is shown that, independently on the particular interpretation of the time-dependent coefficients, these systems generally admit an exact solution, up to the case of the maximal extension within the classification of Lie-Hamilton systems, for which a superposition rule is constructed. The method provides the algebraic frame to which any SIS epidemic model that preserves the above-mentioned properties is subjected. In particular, we obtain exact solutions for generalized SIS Hamiltonian models based on the book and oscillator algebras, denoted by b 2 and h 4 , respectively. The last generalization corresponds to an SIS system possessing the so-called two-photon algebra symmetry h 6 , according to the embedding chain b 2 h 4 h 6 , for which an exact solution cannot generally be found but a nonlinear superposition rule is explicitly given.

CLC number: 17B66, 34A26, 34C14, 92D25

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AIMS Mathematics
Pages 24025-24052

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Cite this article:
Campoamor-Stursberg R, Fernández-Saiz E, Herranz FJ. Exact solutions and superposition rules for Hamiltonian systems generalizing time-dependent SIS epidemic models with stochastic fluctuations. AIMS Mathematics, 2023, 8(10): 24025-24052. https://doi.org/10.3934/math.20231225

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Received: 14 May 2023
Revised: 18 July 2023
Accepted: 24 July 2023
Published: 15 October 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)