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.

疫情期间，各类防疫信息同时在人群中传播。与此同时，大众媒体也争相报道与疫情有关的信息。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和流行病共同演化传播的动态特征。我们在两层网络的框架下构建了协同进化模型，并借助微马尔可夫链方法获得了动力学方程和流行病临界点。疫情临界点的表达表明，正负信息对疫情临界点有直接影响。而且，大众媒体通过干扰疫情相关信息的传播，可以间接影响疫情规模和疫情临界点。通过大量的数值实验，我们检验了动力学方程的准确性和流行病临界点的表达，表明动力学方程可以很好地描述协同进化传播的动力学特征，并且能够准确地计算出流行病临界点。通过派生表达式。实验结果表明，加快积极信息传播，提高大众媒体的宣传力度，可以有效抑制疫情蔓延。有趣的是，加快负面信息传播的方式，也可以在正面信息难以传播的情况下，在一定程度上缓解疫情。目前的研究结果可以在疫情相关信息传播的基础上为疫情防控提供一些有用的线索。