A multilayer network model of the coevolution of the spread of a disease and competing opinions
A multilayer network model of the coevolution of the spread of a disease and competing opinions
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DOI：
10.1142/s0218202521500536
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发表时间：
2021-11-01
影响因子：
3.5
通讯作者：
Porter, Mason A.
中科院分区：
文献类型：
--
作者：
Peng, Kaiyan;Lu, Zheng;Porter, Mason A.
在19日期的大流行期间，关于物理距离的矛盾观点席卷了社交媒体，影响了人类的行为和COVID-19的传播。受这种现象的启发，我们构建了一个两层多路复用网络，用于疾病和观点相互冲突的耦合。我们将每个过程建模为一种传染性。在一层中，我们考虑了两种观点的同时演变 - 亲物理 - 势态和反物质抗距离 - 它们相互竞争，彼此之间具有相互的免疫力。该疾病在另一层演变，当个人采用亲物理持续（分别是抗物理抗态）的意见时，个人（分别更有可能，更有可能）被感染。我们通过将单层对近似值推广到多层网络来开发平均场类型的近似值；这些近似值与蒙特卡洛模拟对广泛参数和几个网络结构非常吻合。通过数值模拟，我们说明了意见动力学对疾病在两种冲突意见之间以及观点和疾病之间的复杂相互作用中传播的影响。我们发现，延长个人意见的持续时间可能有助于抑制疾病的传播，并且我们证明，增加节点学位的跨层相关性或层内相关性可能会导致较少的人感染该疾病。
During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions - pro-physical-distancing and anti-physical-distancing - that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease.