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Early epidemic spread, percolation and Covid-19

机译:早期流行病,渗透和Covid-19

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摘要

Human to human transmissible infectious diseases spread in a population using human interactions as its transmission vector. The early stages of such an outbreak can be modeled by a graph whose edges encode these interactions between individuals, the vertices. This article attempts to account for the case when each individual entails in different kinds of interactions which have therefore different probabilities of transmitting the disease. The majority of these results can be also stated in the language of percolation theory. The main contributions of the article are: (1) Extend to this setting some results which were previously known in the case when each individual has only one kind of interactions. (2) Find an explicit formula for the basic reproduction number R-0 which depends only on the probabilities of transmitting the disease along the different edges and the first two moments of the degree distributions of the associated graphs. (3) Motivated by the recent Covid-19 pandemic, we use the framework developed to compute the R-0 of a model disease spreading in populations whose trees and degree distributions are adjusted to several different countries. In this setting, we shall also compute the probability that the outbreak will not lead to an epidemic. In all cases we find such probability to be very low if no interventions are put in place.
机译:人类传染性传染病的人类传播的传染病使用人类相互作用作为其传输载体传播。这种爆发的早期阶段可以由图形建模的,该图形是其边缘编码各个之间的这些相互作用,顶点。本文试图考虑到每个人需要不同种类的相互作用,这些相互作用具有不同的传递疾病的概率。这些结果的大多数也可以用渗透理论的语言说明。文章的主要贡献是:(1)延伸到这一设置一些结果在每个人只有一种互动的情况下。 (2)找到基本再现号R-0的明确公式,其仅取决于沿着不同边缘传递疾病的概率和相关图的程度分布的前两个瞬间。 (3)由最近的Covid-19大流行激励,我们使用开发的框架来计算群体的模型疾病的R-0,其树木和程度分布调整到几个不同的国家。在这个环境中,我们还应该计算爆发不会导致流行病的概率。在所有情况下,如果没有解决干预措施,我们发现这种概率非常低。

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