Brabers J H V J
Biol Methods Protoc. 2023 Jun 20;8(1):bpad010. doi: 10.1093/biomethods/bpad010. eCollection 2023.
This article deals with the spread of infectious diseases from a physics perspective. It considers a population as a network of nodes representing the population members, linked by network edges representing the (social) contacts of the individual population members. Infections spread along these edges from one node (member) to another. This article presents a novel, modified version of the SIR compartmental model, able to account for typical network effects and percolation phenomena. The model is successfully tested against the results of simulations based on Monte-Carlo methods. Expressions for the (basic) reproduction numbers in terms of the model parameters are presented, and justify some mild criticisms on the widely spread interpretation of reproduction numbers as being the number of secondary infections due to a single active infection. Throughout the article, special emphasis is laid on understanding, and on the interpretation of phenomena in terms of concepts borrowed from condensed-matter and statistical physics, which reveals some interesting analogies. Percolation effects are of particular interest in this respect and they are the subject of a detailed investigation. The concept of herd immunity (its definition and nature) is intensively dealt with as well, also in the context of large-scale vaccination campaigns and waning immunity. This article elucidates how the onset of herd-immunity can be considered as a second-order phase transition in which percolation effects play a crucial role, thus corroborating, in a more pictorial/intuitive way, earlier viewpoints on this matter. An exact criterium for the most relevant form of herd-immunity to occur can be derived in terms of the model parameters. The analyses presented in this article provide insight in how various measures to prevent an epidemic spread of an infection work, how they can be optimized and what potentially deceptive issues have to be considered when such measures are either implemented or scaled down.
本文从物理学角度探讨传染病的传播。它将人群视为一个由代表人群成员的节点组成的网络,这些节点通过代表个体人群成员(社会)接触的网络边相连。感染沿着这些边从一个节点(成员)传播到另一个节点。本文提出了一种新颖的、改进的SIR分区模型,该模型能够考虑典型的网络效应和渗流现象。该模型已根据基于蒙特卡罗方法的模拟结果成功进行了测试。给出了根据模型参数表示的(基本)繁殖数的表达式,并对将繁殖数广泛解释为由于单个活跃感染导致的二次感染数量的观点提出了一些温和的批评。在整篇文章中,特别强调了理解以及用从凝聚态和统计物理学借用的概念来解释现象,这揭示了一些有趣的类比。在这方面,渗流效应特别令人感兴趣,并且是详细研究的主题。还深入探讨了群体免疫的概念(其定义和性质),同样是在大规模疫苗接种运动和免疫力下降的背景下。本文阐明了如何将群体免疫的出现视为二阶相变,其中渗流效应起着关键作用,从而以更形象/直观的方式证实了关于此事的早期观点。可以根据模型参数得出最相关形式的群体免疫发生的精确标准。本文所呈现的分析有助于深入了解预防感染流行传播的各种措施是如何起作用的、如何进行优化,以及在实施或缩减这些措施时必须考虑哪些潜在的欺骗性问题。