Wang Zhishuang, Xia Chengyi
Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384 China.
The Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China.
Nonlinear Dyn. 2020;102(4):3039-3052. doi: 10.1007/s11071-020-06021-7. Epub 2020 Nov 2.
During epidemic outbreaks, there are various types of information about epidemic prevention disseminated simultaneously among the population. Meanwhile, the mass media also scrambles to report the information related to the epidemic. Inspired by these phenomena, we devise a model to discuss the dynamical characteristics of the co-evolution spreading of multiple information and epidemic under the influence of mass media. We construct the co-evolution model under the framework of two-layered networks and gain the dynamical equations and epidemic critical point with the help of the micro-Markov chain approach. The expression of epidemic critical point show that the positive and negative information have a direct impact on the epidemic critical point. Moreover, the mass media can indirectly affect the epidemic size and epidemic critical point through their interference with the dissemination of epidemic-relevant information. Though extensive numerical experiments, we examine the accuracy of the dynamical equations and expression of the epidemic critical point, showing that the dynamical characteristics of co-evolution spreading can be well described by the dynamic equations and the epidemic critical point is able to be accurately calculated by the derived expression. The experimental results demonstrate that accelerating positive information dissemination and enhancing the propaganda intensity of mass media can efficaciously restrain the epidemic spreading. Interestingly, the way to accelerate the dissemination of negative information can also alleviate the epidemic to a certain extent when the positive information hardly spreads. Current results can provide some useful clues for epidemic prevention and control on the basis of epidemic-relevant information dissemination.
在疫情爆发期间，关于防疫的各类信息在人群中同时传播。与此同时，大众媒体也竞相报道与疫情相关的信息。受这些现象的启发，我们设计了一个模型来讨论在大众媒体影响下多种信息与疫情共同演化传播的动力学特征。我们在双层网络框架下构建了共同演化模型，并借助微观马尔可夫链方法得到了动力学方程和疫情临界点。疫情临界点的表达式表明，正面和负面信息对疫情临界点有直接影响。此外，大众媒体可以通过干扰与疫情相关信息的传播来间接影响疫情规模和疫情临界点。通过广泛的数值实验，我们检验了动力学方程和疫情临界点表达式的准确性，结果表明动力学方程能够很好地描述共同演化传播的动力学特征，并且通过推导的表达式能够准确计算疫情临界点。实验结果表明，加速正面信息传播和增强大众媒体的宣传力度能够有效抑制疫情传播。有趣的是，当正面信息难以传播时，加速负面信息传播的方式在一定程度上也能缓解疫情。当前结果可为基于与疫情相关信息传播的疫情防控提供一些有用线索。
