从偏好函数出发构建同类混合网络,并应用于流行病的传播

IF 1.9 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Frontiers in Physics Pub Date : 2024-08-05 DOI:10.3389/fphy.2024.1435767
Razvan G. Romanescu
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引用次数: 0

摘要

在根据配置模型(Configuration Model)构建的网络上,疾病传播的区隔模型得到了很好的研究,即单个节点的度分布是指定的,但连接是随机的。研究表明,在这种 "一阶 "网络上的传播动力学与传统的大规模行动假设下的流行病有很大不同。同类性,即节点根据程度优先混合,是一种二阶属性,被认为会影响流行病的轨迹。我们首先展示了同类混合是如何通过个人偏好与较低或较高程度的其他人建立联系而产生的,并提出了构建这种网络的算法。然后,我们通过模拟研究了这种网络结构如何促进或抑制扩散过程,如传染病的传播。
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Building a network with assortative mixing starting from preference functions, with application to the spread of epidemics
Compartmental models of disease spread have been well studied on networks built according to the Configuration Model, i.e., where the degree distribution of individual nodes is specified, but where connections are made randomly. Dynamics of spread on such “first order” networks were shown to be profoundly different compared to epidemics under the traditional mass action assumption. Assortativity, i.e., the preferential mixing of nodes according to degree, is a second order property that is thought to impact epidemic trajectory. We first show how assortative mixing can come about from individual preferences to connect with others of lower or higher degree, and propose an algorithm for constructing such a network. We then investigate via simulation how this network structure favors or inhibits diffusion processes, such as the spread of an infectious disease.
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来源期刊
Frontiers in Physics
Frontiers in Physics Mathematics-Mathematical Physics
CiteScore
4.50
自引率
6.50%
发文量
1215
审稿时长
12 weeks
期刊介绍: Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.
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