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由水文气候变率和记忆性引发的周期性流行和极端爆发。

Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory.

作者信息

Hooshyar Milad, Wagner Caroline E, Baker Rachel E, Metcalf C Jessica E, Grenfell Bryan T, Porporato Amilcare

机构信息

CEE, PEI, and PIIRS, Princeton University, Princeton, NJ, USA.

Department of Bioengineering, McGill University, Montreal, Canada.

出版信息

J R Soc Interface. 2020 Oct;17(171):20200521. doi: 10.1098/rsif.2020.0521. Epub 2020 Oct 21.

Abstract

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme , rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.

摘要

一个生态水文动力学的极简模型与著名的易感-感染-康复流行病学模型相耦合,以探究水文气候对感染动态和极端疫情爆发的控制作用。由此产生的HYSIR模型揭示了噪声诱导分岔的存在,这种分岔会在感染动态中产生振荡。控制方程的线性化使得能够根据流行病学参数(如传播率和康复率)以及水文参数(即土壤水分衰减率或记忆)得出感染周期性的解析表达式。完整随机非线性系统的数值模拟表明,在特定的水文气候条件组合下会出现极端疫情爆发,这些条件单独来看都不极端,而不是由单一的重大气候事件导致。这些组合取决于水文变量与传播率之间假定的函数关系。我们的结果强调了水文气候历史和系统记忆在评估严重疫情爆发风险中的重要性。

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