ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


For citation:

Rassadin A. E. An asymptotic solution for the SIS epidemic model, taking into account migration and diffusion. Izvestiya VUZ. Applied Nonlinear Dynamics, 2024, vol. 32, iss. 6, pp. 908-920. DOI: 10.18500/0869-6632-003141, EDN: VMCMSE

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
Language: 
Russian
Article type: 
Article
UDC: 
517.958:57, 517.956.4
EDN: 

An asymptotic solution for the SIS epidemic model, taking into account migration and diffusion

Autors: 
Rassadin Aleksandr Eduardovich, Nizhny Novgorod Mathematical Society
Abstract: 

The purpose of this work is to propose and investigate a simple and effective model of an epidemic in an animal population that takes into account migration along the plane of both diseased and healthy individuals. Within the framework of this model, the spatial migration of a population is described by introducing both diffusion and advective terms into its equations.

Methods. In this paper, a method of many scales was used to find an asymptotic solution to the system of equations of the epidemic. Solutions of auxiliary linear equations of the parabolic type arising during this procedure were found using the Poisson integral. The simplification of the initial system of equations of the model is based on the assumption that the sum of densities of healthy and sick individuals on a single-connected region of large diameter on the plane is constant at the initial moment of time.

Results. It is shown that in this case, designed for a slowly changing initial density of sick individuals concentrated inside this area at a considerable distance from its boundaries, the asymptotic solution of the model describes the effect of merging several spatially spaced small outbreaks of the disease into one large outbreak during migration of the entire population as a whole. In particular, for such an initial density obtained by the functional transformation of a Gaussian, a circular plateau is formed over long periods with an effective radius that grows linearly over time.

Conclusion. The constructed asymptotic solution of the epidemic model proposed in this paper is simple in form and describes the transfer of the disease on a locally flat area of the earth’s surface without the use of numerical methods. This solution is convenient when describing the migration of a sick population under the influence of flooding, forest fire, man-made disaster with contamination of the area, etc.
 

Reference: 
  1. Lotka AJ. Elements of physical biology. Williams & Wilkins; 1925. 460 p.
  2. Volterra V. Variazioni e fluttuazioni del numero d’individui in specie animali conviventi. Memoria della Reale Accademia Nazionale dei Lincei. 1926;2:31–113.
  3. Bazykin AD. Mathematical biophysics of interacting populations. M.: Nauka; 1985. 181 p. (in Russian).
  4. Riznichenko GYu. Lectures on mathematical models in biology. M.-Izhevsk: Institute of Computer Research, SPC “Regular and Chaotic Dynamics”; 2010. 560 p. (in Russian).
  5. Frisman EYa, Kulakov MP, Revutskaya OL, Zhdanova OL, Neverova GP. The key approaches and review of current researches on dynamics of structured and interacting populations. Computer Research and Modeling. 2019;11(1):119–151. DOI: 10.20537/2076-7633-2019-11-1-119-151.
  6. Belotelov NV, Konovalenko IA. Modeling the impact of mobility of individuals on space-time dynamics of a population by means of a computer model. Computer Research and Modeling. 2016;8(2):297–305. DOI: 10.20537/2076-7633-2016-8-2-297-305.
  7. Kulakov MP, Frisman EYa. Approaches to study of multistability in spatio-temporal dynamics of two-age population. Izvestiya VUZ. Applied Nonlinear Dynamics. 2020;28(6):653–678. DOI: 10.18500/0869-6632-2020-28-6-653-678.
  8. Brauer F, Castillo-Chavez C, Feng Z. Mathematical models in epidemiology. Springer Science + Business Media, LLC, part of Springer Nature; 2019. 619 p. DOI: 10.1007/978-1-4939-9828-9.
  9. Kant S, Kumar V. Stability analysis of predator–prey system with migrating prey and disease infection in both species. Applied Mathematical Modelling. 2017;42:509–539. DOI: 10.1016/j.apm.2016.10.003.
  10. Shabunin AV. SIRS-model with dynamic regulation of the population: Probabilistic cellular automata approach. Izvestiya VUZ, Applied Nonlinear Dynamics. 2019;27(2):5–20. DOI: 10.18500/0869-6632-2019-27-2-5-20.
  11. Arif M, Abodayeh K, Ejaz A. On the stability of the diffusive and non-diffusive predator-prey system with consuming resources and disease in prey species. Mathematical Biosciences and Engineering. 2023;20(3):5066–5093. DOI: 10.3934/mbe.2023235.
  12. Kermack WO, McKendrick AG. Contributions to the mathematical theory of epidemics. II. — The problem of endemicity. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 1932;138(834):55–83. DOI: 10.1098/rspa.1932.0171.
  13. Aristov VV, Stroganov AV, Yastrebov AD. Application of the kinetic type model for study of a spatial spread of COVID-19. Computer Research and Modeling. 2021; 13(3):611–627 (in Russian). DOI: 10.20537/2076-7633-2021-13-3-611-627.
  14. Bugrov VO, Rassadin AE. The model of the spread of a pandemic with two stable states. Proceedings of the International Scientific Youth School-Seminar “Mathematical Modeling, Numerical Methods and Software complexes” named after E.V. Voskresensky (Saransk, July 14-18, 2022). Saransk: SVMO Publ.; 2022. P. 40–48. (in Russian).
  15. Barwolff G. A local and time resolution of the COVID-19 propagation — a two-dimensional  approach for Germany including diffusion phenomena to describe the spatial spread of the COVID-19 pandemic. Physics. 2021;3:536–548. DOI: 10.3390/physics3030033.
  16. Viguerie A, Veneziani A, Lorenzo G, Baroli D, Aretz-Nellesen N, Patton A, Yankeelov TE, Reali A, Hughes TJR, Auricchio F. Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study. Computational Mechanics. 2020;66:1131–1152. DOI: 10.1007/s00466-020-01888-0.
  17. Tikhonov AN, Samarskii AA. Equations of mathematical physics. M.: Nauka; 1966. 724 p. (in Russian).
  18. Kolmogorov AN, Petrovsky IG, Piskunov NS. Etude de lequation de la diffusion avec croissancede la quantite de matiere et son application ` a un probl ` eme biologique. Moscou Univ. Bull. Math. 1937;1(6):1–26.
  19. Berman VS. Asymptotic solution of a nonstationary problem on the propagation of the front of chemical reaction. Dokl. Akad. Nauk SSSR. 1978;242(2):265–267. (in Russian).
  20. Kardar M, Parisi G, Zhang YC. Dynamical scaling of growing interfaces. Physical Review Letters. 1986;56:889–892. DOI: 10.1103/PhysRevLett.56.889.
Received: 
25.06.2024
Accepted: 
04.11.2024
Available online: 
20.11.2024
Published: 
29.11.2024