恐惧因素对具有标准发病率的生态流行病学系统动态的影响

IF 8.8 3区 医学 Q1 Medicine
Chunmei Zhang
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引用次数: 0

摘要

为了保护濒临灭绝的猎物,生态学家建议将寄生虫引入捕食者体内,在实践中达到了预期目的。那么,如何从理论上解释这种方法的内在机制并验证其有效性呢?针对这一问题,我们在本文中提出了一个具有标准发病率和反捕食行为的生态流行病学系统,即捕食者种群被寄生虫感染。我们证明了该系统均衡点的存在性和局部稳定性,并验证了霍普夫分岔的发生。理论和数值结果表明,恐惧效应会降低捕食者种群的密度,但对猎物种群的密度没有影响。此外,恐惧成本不仅可能打破系统平衡的稳定性,还可能促使平衡由不稳定变为稳定。基于理论分析,我们证实在捕食者种群中引入寄生虫是保护濒危猎物的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The effect of the fear factor on the dynamics of an eco-epidemiological system with standard incidence rate

In order to protect endangered prey, ecologists suggest introducing parasites into predators which have achieved the expected goal in practice. Then how to explain the inherent mechanism and validate the effectiveness of this approach theoretically? In response to this question, we propose an eco-epidemiological system with the standard incidence rate and the anti-predator behavior in this paper, where the predator population is infected by parasites. We show the existence and local stability of equilibria for the system, and verify the occurrence of Hopf bifurcation. Theoretical and numerical results suggest that the fear effect reduces the density of the predator population but has no effect on the density of prey population. In addition, the cost of fear may not only break the stability of the equilibrium of the system, but also induce the equilibrium to change from unstable to stable. Based on the theoretical analysis, we confirm that introducing parasites into the predator population is an effective method to protect endangered prey.

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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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