Study On Disease Transmission Under The Influence Of Information Diffusion

With the development of network science, epidemic spreading process has been a hot topic in the study of complex network. The study of epidemic processes on network can reveal the propagation of diseases, predict the trend of epidemic, which are very important to the prevention and control of diseases. In this work, we construct epidemic models by analyzing the influence factors of epidemic spreading.The impact that information diffusion has on epidemic spreading has recently attracted much attention. As a disease begins to spread in the population, information about the disease is transmitted to others, which in turn has an effect on the spread of disease. In the second chapter, using empirical results of the propagation of diseases(H7N9 and dengue)and information about these diseases, we clearly show that the spreading dynamics of the two-types of processes influence each other. We build a mathematical model in which both types of spreading dynamics are described using the SIS process in order to illustrate the influence of information diffusion on epidemic spreading. Both the simulation results and the pairwise analysis reveal that information diffusion can increase the threshold of an epidemic outbreak, decrease the final fraction of infected individuals and significantly decrease the rate at which the epidemic propagates. Additionally, we find that the multi-outbreak phenomena of epidemic spreading, along with the impact of information diffusion, is consistent with the empirical results.Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In the third chapter, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible(infected) individuals who have abilities to recognize the disease would break the links of their infected(susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice give a visual representation about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, oscillatory, bistable and endemic, to understand the evolution of the observed dynamical behaviors.A large number of studies have focused on investigating the impacts of network topology on the spreading dynamics. However, the weighted network is very common in real systems,and we attempt to study the role of edge weights on epidemic spreading. In the fourth chapter, the spreading process was presented as the SIS model and three edge-breaking strategies according to the weight of the SI links were performed simultaneously, which was used to illustrate the influence of the edge weights. Simulation results on three real networks showed the different spreading patterns of different edge-breaking strategies, which in turn indicated the influence of edge weights on the spreading process. Therefore we can take different measures at different periods according to the edge weights to impede the epidemic. In addition, the detailed analyses of relationship between the edge weight and the network structure was given to interpret the role of edge weights in the epidemic spreading process.