Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks
Le He, Linhe Zhu
Abstract
Abstract 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.