Spatial Disease Propagation With Hubs

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-02-24 DOI:10.1109/TNSE.2025.3545386

Ke Feng;Martin Haenggi

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引用次数: 0

Abstract

Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in

$\mathscr {R}^{d}$

and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function

$f$

. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function

$f$

, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.

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具有枢纽的空间疾病传播

身体接触或接近往往是传染病传播的必要条件。公共目的地，通常被称为枢纽或兴趣点，可以说是通过空气传播的疾病传播类型最有效的地点。在这项工作中，我们通过$\mathscr {R}^{d}$中的随机空间点过程对个体（代理）和公共目的地（枢纽）的位置进行建模，并关注通过访问公共枢纽的代理进行疾病传播。代理访问集线器的概率取决于它们通过连接函数$f$的距离。该系统用随机二部几何图（RBG）表示。我们研究了一般连接函数的RBG图的度和渗透率。我们表明，渗透枢纽的临界密度是由连接函数$f$的支持决定的，这揭示了长途旅行（或其限制）在疾病传播中的关键作用。

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

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.