The coronavirus disease 2019 (COVID-19) has been widely spread around the world, and the control and behavior dynamics are still one of the important research directions in the world. Based on the characteristics of COVID-19's spread, a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information. In the layer of epidemic spreading, the nodes can be divided into five categories, where the topology of the network represents the physical contact relationship of the population. The topological structure of the upper network shows the information interaction among the nodes, which can be divided into aware and unaware states. Awareness will make people play a positive role in preventing the epidemic diffusion, influencing the spread of the disease. Based on the above model, we have established the state transition equation through the microscopic Markov chain approach (MMCA), and proposed the propagation threshold calculation method under the epidemic model. Furthermore, MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network, respectively. The current results will be beneficial to the study of COVID-19, and propose a more rational and effective model for future research on epidemics.

2019年冠状病毒病（COVID-19）已在世界范围内广泛传播，控制和行为动态仍然是世界上重要的研究方向之一。根据COVID-19传播的特点，提出了一种基于多重网络的疾病意识耦合模型，研究并模拟了COVID-19传播行为与相关信息之间的相互作用。在流行病的传播层中，节点可分为五类，其中网络的拓扑结构表示人口的物理接触关系。上层网络的拓扑结构显示了节点之间的信息交互，可以将其分为感知状态和非感知状态。意识将使人们在防止流行病扩散方面发挥积极作用，影响疾病的传播。在上述模型的基础上，通过微观马尔可夫链法（MMCA）建立了状态转移方程，并提出了基于流行病模型的传播阈值计算方法。此外，分别在静态网络和动态网络上模拟了MMCA迭代和蒙特卡洛方法。目前的结果将有利于COVID-19的研究，并为今后的流行病学研究提供了更为合理和有效的模型。MMCA迭代和蒙特卡洛方法分别在静态网络和动态网络上进行了仿真。目前的结果将有利于COVID-19的研究，并为今后的流行病学研究提供了更为合理和有效的模型。MMCA迭代和蒙特卡洛方法分别在静态网络和动态网络上进行了仿真。目前的结果将有利于COVID-19的研究，并为今后的流行病学研究提供了更为合理和有效的模型。