在配置模型网络上对连续两次SIR流行病的传播进行建模。

IF 2.3 4区 数学 Q2 BIOLOGY
Frank Ball, Abid Ali Lashari, David Sirl, Pieter Trapman
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引用次数: 0

摘要

我们提出了在同一网络结构人群中两个连续SIR(易感→感染→恢复)流行病的随机模型。在第一次流行期间感染的个体可能对第二次流行具有(部分)免疫力。第一次流行是通过一个键渗透模型来分析的,而第二次流行是通过一个三类型分支过程来近似的,在这个过程中,个体的类型取决于他们在第一次流行所使用的渗透簇中的位置。这种分支过程近似使我们能够在大人口限制和第一次流行病大爆发的条件下,计算第二次流行病大爆发的阈值参数和概率。第二个分支过程近似值使我们能够计算被这种第二次大爆发感染的人口比例。我们通过先前文献中出现的一些具体案例来说明我们的结果,并表明我们的渐近结果对有限总体给出了很好的近似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modelling the spread of two successive SIR epidemics on a configuration model network.

Modelling the spread of two successive SIR epidemics on a configuration model network.

Modelling the spread of two successive SIR epidemics on a configuration model network.

Modelling the spread of two successive SIR epidemics on a configuration model network.

We present a stochastic model for two successive SIR (Susceptible Infectious Recovered) epidemics in the same network structured population. Individuals infected during the first epidemic might have (partial) immunity for the second one. The first epidemic is analysed through a bond percolation model, while the second epidemic is approximated by a three-type branching process in which the types of individuals depend on their position in the percolation clusters used for the first epidemic. This branching process approximation enables us to calculate, in the large population limit and conditional upon a large outbreak in the first epidemic, a threshold parameter and the probability of a large outbreak for the second epidemic. A second branching process approximation enables us to calculate the fraction of the population that are infected by such a second large outbreak. We illustrate our results through some specific cases which have appeared previously in the literature and show that our asymptotic results give good approximations for finite populations.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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