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多重网络中疾病和信息传播的速度不同。

Disease and information spreading at different speeds in multiplex networks.

机构信息

Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), 1900 La Plata, Argentina.

Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, São Paulo, Brazil.

出版信息

Phys Rev E. 2020 Aug;102(2-1):022312. doi: 10.1103/PhysRevE.102.022312.

Abstract

Nowadays, one of the challenges we face when carrying out modeling of epidemic spreading is to develop methods to control disease transmission. In this article we study how the spreading of knowledge of a disease affects the propagation of that disease in a population of interacting individuals. For that, we analyze the interaction between two different processes on multiplex networks: the propagation of an epidemic using the susceptible-infected-susceptible dynamics and the dissemination of information about the disease-and its prevention methods-using the unaware-aware-unaware dynamics, so that informed individuals are less likely to be infected. Unlike previous related models where disease and information spread at the same time scale, we introduce here a parameter that controls the relative speed between the propagation of the two processes. We study the behavior of this model using a mean-field approach that gives results in good agreement with Monte Carlo simulations on homogeneous complex networks. We find that increasing the rate of information dissemination reduces the disease prevalence, as one may expect. However, increasing the speed of the information process as compared to that of the epidemic process has the counterintuitive effect of increasing the disease prevalence. This result opens an interesting discussion about the effects of information spreading on disease propagation.

摘要

如今,我们在进行传染病传播建模时面临的挑战之一是开发控制疾病传播的方法。在本文中,我们研究了疾病知识的传播如何影响人群中相互作用个体中疾病的传播。为此,我们分析了在多重网络上两种不同过程之间的相互作用:使用易感-感染-易感动力学传播传染病,以及使用不知不觉-意识到-不知不觉动力学传播疾病及其预防方法的信息传播,从而使知情个体不太可能感染。与之前在同一时间尺度上同时传播疾病和信息的相关模型不同,我们在这里引入了一个参数来控制两个过程的相对传播速度。我们使用平均场方法研究了该模型的行为,该方法得到的结果与同质复杂网络上的蒙特卡罗模拟非常吻合。我们发现,增加信息传播的速度会降低疾病的流行率,这是可以预期的。然而,与传染病过程相比,增加信息过程的速度会产生增加疾病流行率的反直觉效果。这一结果引发了关于信息传播对疾病传播影响的有趣讨论。

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