A risk-induced dispersal strategy of the infected population for a disease-free state in the SIS epidemic model.

IF 1.8 4区 数学 Q3 ECOLOGY
Journal of Biological Dynamics Pub Date : 2024-12-01 Epub Date: 2024-05-08 DOI:10.1080/17513758.2024.2352359
Wonhyung Choi, Inkyung Ahn
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引用次数: 0

Abstract

This article proposes a dispersal strategy for infected individuals in a spatial susceptible-infected-susceptible (SIS) epidemic model. The presence of spatial heterogeneity and the movement of individuals play crucial roles in determining the persistence and eradication of infectious diseases. To capture these dynamics, we introduce a moving strategy called risk-induced dispersal (RID) for infected individuals in a continuous-time patch model of the SIS epidemic. First, we establish a continuous-time n-patch model and verify that the RID strategy is an effective approach for attaining a disease-free state. This is substantiated through simulations conducted on 7-patch models and analytical results derived from 2-patch models. Second, we extend our analysis by adapting the patch model into a diffusive epidemic model. This extension allows us to explore further the impact of the RID movement strategy on disease transmission and control. We validate our results through simulations, which provide the effects of the RID dispersal strategy.

SIS 流行病模型中无疾病状态下受感染人群的风险诱导扩散策略。
本文提出了空间易感-感染-易感(SIS)流行病模型中受感染个体的扩散策略。空间异质性的存在和个体的移动在决定传染病的持续和根除方面起着至关重要的作用。为了捕捉这些动态变化,我们在 SIS 流行病的连续时间斑块模型中引入了一种称为风险诱导分散(RID)的受感染个体移动策略。首先,我们建立了一个连续时间 n 补丁模型,并验证了 RID 策略是实现无疾病状态的有效方法。通过对 7 个斑块模型的模拟和 2 个斑块模型的分析结果,我们证实了这一点。其次,我们通过将斑块模型调整为扩散流行病模型来扩展我们的分析。这一扩展使我们能够进一步探索 RID 移动策略对疾病传播和控制的影响。我们通过模拟验证了我们的结果,并提供了 RID 传播策略的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
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