2023/03/08 by Yuqi Li, Lihua Zhang, Li, Yuqi +1
Mathematics · Medicine · #COVID-19 epidemiological studies #FOS: Mathematics #Mathematical Biology Tumor Growth #Mathematical and Theoretical Epidemiology and Ecology Models #Probability (math.PR)
paper · pdf · doi:10.48550/arxiv.2303.04899
openalex publication_date 2023/03/08 · openalex created_date 2023/03/12 · openalex updated_date 2026/07/28
The study of epidemic models plays an important role in mathematical epidemiology. There are many researches on epidemic models using ordinary differential equations, partial differential equations or stochastic differential equations. In contrast to these researches, our work analyzes the SEIRS (Susceptible-Exposed-Infected-Recovered-Susceptible) model using stochastic partial differential equations. Specifically, we consider the effects of spatial variables and space-time white noise on the epidemic model. In the means of mild solution, we construct a contraction mapping and prove the well-posedness for the stochastic partial differential equation using the fixed point argument and the factorization method. We also find sufficient conditions for permanence and extinction to determine the longtime behavior of the solution. We hope that this work can provide some new ideas for the study of increasingly complex epidemic models and related numerical simulations.