Hui Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hui Wang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Dongxu Wang
School of Mathematics and Information Science, Shandong Technology and Business University, Yantai 264003, China
1 shared publication - Yuxiao Zhao
School of Mathematics and Information Science, Shandong Technology and Business University, Yantai 264005, China
1 shared publication
Latest Publications
- Open Access
- 24 September 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (19) , 2966
The Dynamic Behavior of a Stochastic SEIRM Model of COVID-19 with Standard Incidence Rate
This paper studies the dynamic behavior of a stochastic SEIRM model of COVID-19 with a standard incidence rate. The existence of global solutions for dynamic system models is proven by integrating stochastic process theory and the concept of stopping times, together with the contradiction method. Moreover, we construct appropriate Lyapunov functions to analyze system stability and apply Dynkin’s formula and Fatou’s lemma to handle stopping times and expectations of stochastic processes. Notably, the extinction study provides mathematical proof that under the given system dynamics, the total population does not grow indefinitely but tends to stabilize over time. The properties of the diffusion matrix are harnessed to guarantee the system’s stationary distribution. Conclusively, numerical simulations confirm the model’s extinction outcomes.
- 24 September 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (19) , 2966
Open AccessThe Dynamic Behavior of a Stochastic SEIRM Model of COVID-19 with Standard Incidence Rate
This paper studies the dynamic behavior of a stochastic SEIRM model of COVID-19 with a standard incidence rate. The existence of global solutions for dynamic system models is proven by integrating stochastic process theory and the concept of stopping times, together with the contradiction method. Moreover, we construct appropriate Lyapunov functions to analyze system stability and apply Dynkin’s formula and Fatou’s lemma to handle stopping times and expectations of stochastic processes. Notably, the extinction study provides mathematical proof that under the given system dynamics, the total population does not grow indefinitely but tends to stabilize over time. The properties of the diffusion matrix are harnessed to guarantee the system’s stationary distribution. Conclusively, numerical simulations confirm the model’s extinction outcomes.