Dynamics of SIR model with heterogeneous response to intervention policy

IF 1.2 4区 生物学 Q4 ECOLOGY
Dmitrii Rachinskii, Samiha Rouf
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引用次数: 1

Abstract

In classical epidemic theory, behavior is assumed to be stationary. In recent years, epidemic models have been extended to include behaviors that transition in response to the current state of the epidemic. However, it is widely known that human behavior can exhibit strong history-dependence as a consequence of learned experiences. This history-dependence is similar to hysteresis phenomena that have been well-studied in control theory. To illustrate the importance of history-dependence for epidemic theory, we study dynamics of a variant of the SIRS model where individuals exhibit lazy-switch responses to prevalence dynamics, based on the Preisach hysteresis operator. The resulting model can possess a continuum of endemic equilibrium states characterized by different proportions of susceptible, infected and recovered populations. We consider how the limit point of the epidemic trajectory and the infection peak along this trajectory depend on the degree of heterogeneity of the response. Our approach supports the argument that public health responses during the emergence of a new disease can have fundamental long-term consequences for subsequent management efforts.

具有干预策略异构响应的SIR模型动力学
在经典的流行病理论中,行为被认为是平稳的。近年来,流行病模型已经扩展到包括响应当前流行病状态的转变行为。然而,众所周知,作为学习经验的结果,人类行为可以表现出强烈的历史依赖性。这种历史依赖性类似于控制理论中已经得到充分研究的滞后现象。为了说明历史依赖性对流行病理论的重要性,我们研究了SIRS模型的一种变体的动力学,其中个体基于Preisach滞后算子对流行动态表现出惰性开关反应。由此产生的模型可以具有以不同比例的易感、感染和恢复种群为特征的地方性平衡状态的连续体。我们考虑流行轨迹的极限点和沿此轨迹的感染峰值如何取决于响应的异质性程度。我们的方法支持这样一种观点,即在新疾病出现期间的公共卫生反应可以对随后的管理工作产生根本性的长期影响。
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
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
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