The stationary distribution in a class of stochastic SIRS epidemic models with non-monotonic incidence and degenerate diffusion

Tuerxun, Nafeisha; Wen, Buyu; Teng, Zhidong

doi:10.1016/j.matcom.2020.03.008

Cited by 9 publications

(1 citation statement)

References 29 publications

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“…Based on the comprehensive consideration of epidemic characteristics, intervention effects, and medical conditions, referring to the existing research and reference stage of discrete time-delay dynamics model [ 33 – 35 ], combined with the epidemic development process of Guangzhou, the classical infectious disease dynamics model was improved. Moreover, different models and parameters were used to express different stages, and a time-delay SEIR epidemic model was established, which was suitable for Guangzhou.…”

Section: Seir-based Construction Of Emergency Medical Facility Site S...mentioning

confidence: 99%

Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model

Liu

Cao

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Since the outbreak of COVID-19, the rapid construction and operation of Wuhan Vulcan Mountain Hospital and Raytheon Hospital have attracted positive responses from local and international observers. At the same time, it has also highlighted the urgency for the construction of emergency medical facilities for public health emergencies. Before construction, the practical location of medical facilities is the basis for improving the city’s emergency management ability. Based on the classic susceptible, exposed, infected, and recovered (SEIR) epidemic model and epidemic data in Guangzhou, we established a multi-stage time-delay SEIR epidemic model that is suitable for epidemic research in Guangzhou. According to the results of the model, the five areas with the highest number of infected patients were identified, which included Baiyun District, Panyu District, Haizhu District, Tianhe District, and Zengcheng District. We then centralized infected individuals at five demand points. Based on the distribution of these points and by combining the characteristics of the emergency medical facilities, we built and solved the set covering location decision model, and considered the economy, society, and environment as the starting points to optimize the site location. Finally, based on simulations, we concluded that appropriate site selection can increase the time required to reach the maximum number of patients and reduce the proportion of infected and exposed people by 11.3% and 1.11%, respectively. This is indicative of the effectiveness of the site selection model and the rational selection of facility points in this study. It solves the optimization problem of the location decision of emergency medical facilities for public health emergencies in China, and also provides some valuable references for site selection decisions of emergency medical facilities in other areas.

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Section: Seir-based Construction Of Emergency Medical Facility Site S...mentioning

confidence: 99%

Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model

Liu

Cao

Zhang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Threshold Dynamics for Infection Age-Structured Epidemic Models with Spatial Diffusion and Degenerate Diffusion

Huo

Yuan

2023

J Dyn Diff Equat

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The Impact of Nonlinear Stochastic Perturbation on Dynamical Behavior of an HIV Infection System

Sun

2023

J Syst Sci Complex

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The stationary distribution in a class of stochastic SIRS epidemic models with non-monotonic incidence and degenerate diffusion

Cited by 9 publications

References 29 publications

Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model

Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model

Threshold Dynamics for Infection Age-Structured Epidemic Models with Spatial Diffusion and Degenerate Diffusion

The Impact of Nonlinear Stochastic Perturbation on Dynamical Behavior of an HIV Infection System

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