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耦合信息-疫情传播动力学与选择性大众媒体

Coupled Information-Epidemic Spreading Dynamics with Selective Mass Media.

作者信息

Xian Jiajun, Zhang Zhihong, Li Zongyi, Yang Dan

机构信息

Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China.

出版信息

Entropy (Basel). 2023 Jun 12;25(6):927. doi: 10.3390/e25060927.

Abstract

As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information-epidemic dynamics, while accounting for the promotional effect of mass media in information dissemination, is of significant practical relevance. Nonetheless, in the extant research, scholars predominantly employ an assumption that mass media broadcast to all individuals equally within the network: this assumption overlooks the practical constraint imposed by the substantial social resources required to accomplish such comprehensive promotion. In response, this study introduces a coupled information-epidemic spreading model with mass media that can selectively target and disseminate information to a specific proportion of high-degree nodes. We employed a microscopic Markov chain methodology to scrutinize our model, and we examined the influence of the various model parameters on the dynamic process. The findings of this study reveal that mass media broadcasts directed towards high-degree nodes within the information spreading layer can substantially reduce the infection density of the epidemic, and raise the spreading threshold of the epidemic. Additionally, as the mass media broadcast proportion increases, the suppression effect on the disease becomes stronger. Moreover, with a constant broadcast proportion, the suppression effect of mass media promotion on epidemic spreading within the model is more pronounced in a multiplex network with a negative interlayer degree correlation, compared to scenarios with positive or absent interlayer degree correlation.

摘要

随着大流行的出现,防疫信息在民众中传播,而这些信息的传播与疾病的扩散相互作用。大众媒体在促进与疫情相关信息的传播方面发挥着关键作用。研究信息-疫情耦合动态,同时考虑大众媒体在信息传播中的促进作用,具有重要的现实意义。然而,在现有研究中,学者们主要采用一种假设,即大众媒体在网络中对所有个体进行平等传播:这种假设忽略了实现这种全面推广所需的大量社会资源所带来的实际限制。对此,本研究引入了一种带有大众媒体的信息-疫情传播耦合模型,该模型可以有选择地针对特定比例的高度节点进行信息传播。我们采用微观马尔可夫链方法来研究我们的模型,并考察了各种模型参数对动态过程的影响。本研究结果表明,在信息传播层针对高度节点的大众媒体传播可以大幅降低疫情的感染密度,并提高疫情的传播阈值。此外,随着大众媒体传播比例的增加,对疾病的抑制作用会更强。而且,在传播比例恒定的情况下,与层间度相关性为正或不存在的情况相比,大众媒体推广对模型中疫情传播的抑制作用在层间度相关性为负的多重网络中更为明显。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2d/10297725/553936b7656d/entropy-25-00927-g001.jpg

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