Stochastic epidemic model for the dynamics of novel coronavirus transmission-Reference-Cited by-同舟云学术

Stochastic epidemic model for the dynamics of novel coronavirus transmission

Published:2024 Issue:5 Volume:9 Page:12433-12457
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Khan Tahir¹,Rihan Fathalla A.¹,Riaz Muhammad Bilal²³,Altanji Mohamed⁴,Zaagan Abdullah A.⁵,Ahmad Hijaz⁶⁷⁸

Affiliation:

1. Department of Mathematical Sciences, UAE University, Al-Ain, P.O. Box 15551, United Arab Emirates

2. IT4Innovations, VSB – Technical University of Ostrava, Ostrava, Czech Republic

3. Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon

4. Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Saudi Arabia

5. Department of Mathematics, Faculty of Science, Jazan University, P.O. Box 2097, Jazan 45142, Saudi Arabia

6. Department of Mathematics, Faculty of Science, Islamic University of Madinah, Medina 42210, Saudi Arabia

7. Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Mishref, Kuwait

8. Near East University, Operational Research Center in Healthcare, TRNC Mersin 10, Nicosia, 99138, Turkey

Abstract

<abstract><p>Stochastic differential equation models are important and provide more valuable outputs to examine the dynamics of SARS-CoV-2 virus transmission than traditional models. SARS-CoV-2 virus transmission is a contagious respiratory disease that produces asymptomatically and symptomatically infected individuals who are susceptible to multiple infections. This work was purposed to introduce an epidemiological model to represent the temporal dynamics of SARS-CoV-2 virus transmission through the use of stochastic differential equations. First, we formulated the model and derived the well-posedness to show that the proposed epidemiological problem is biologically and mathematically feasible. We then calculated the stochastic reproductive parameters for the proposed stochastic epidemiological model and analyzed the model extinction and persistence. Using the stochastic reproductive parameters, we derived the condition for disease extinction and persistence. Applying these conditions, we have performed large-scale numerical simulations to visualize the asymptotic analysis of the model and show the effectiveness of the results derived.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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