A dynamical study on stochastic reaction diffusion epidemic model with nonlinear incidence rate
Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan
2 Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
3 Department of Mathematics, University of Narowal, Narowal, Pakistan
4 Department of Humanities and Basic Science, MCS, NUST, Islamabad, Pakistan
5 Institute of Applied Mathematics, Graz University of Technology, 8010, Graz, Austria
6 Department of Mathematics, Govt. Maulana Zafar Ali Khan Graduate College Wazirabad, Punjab Higher Education Department (PHED), 54000, Lahore, Pakistan
7 Department of Mathematics, Faculty of Science and Technology, University of the Central Punjab, Lahore, Pakistan
8 Department of Mathematics, Near East University, Mathematics Research Center, Near East Boulevard, 99138, Nicosia, Mersin 10, Turkey
9 Department of Mathematics, Faculty of Science, Firat University, 23119, Elazig, Turkey
10 Department of Medical Research, China Medical University, 40402, Taichung, Taiwan
Accepted: 26 March 2023
Published online: 22 April 2023
The current study deals with the stochastic reaction–diffusion epidemic model numerically with two proposed schemes. Such models have many applications in the disease dynamics of wildlife, human life, and others. During the last decade, it is observed that the epidemic models cannot predict the accurate behavior of infectious diseases. The empirical data gained about the spread of the disease shows non-deterministic behavior. It is a strong challenge for researchers to consider stochastic epidemic models. The effect of the stochastic process is analyzed. So, the SIR epidemic model is considered under the influence of the stochastic process. The time noise term is taken as the stochastic source. The coefficient of the stochastic term is a Borel function, and it is used to control the random behavior in the solutions. The proposed stochastic backward Euler scheme and the proposed stochastic implicit finite difference scheme (IFDS) are used for the numerical solution of the underlying model. Both schemes are consistent in the mean square sense. The stability of the schemes is proven with Von-Neumann criteria and schemes are unconditionally stable. The proposed stochastic backward Euler scheme converges toward a disease-free equilibrium and does not converge toward an endemic equilibrium but also possesses negative behavior. The proposed stochastic IFD scheme converges toward disease-free equilibrium and endemic equilibrium. This scheme also preserves positivity. The graphical behavior of the stochastic SIR model is much similar to the classical SIR epidemic model when noise strength approaches zero. The three-dimensional plots of the susceptible and infected individuals are drawn for two cases of endemic equilibrium and disease-free equilibriums. The efficacy of the proposed scheme is shown in the graphical behavior of the test problem for the various values of the parameters.
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