Dynamics Of Disease Transmission On Complex Networks Based On Effective Control

Since ancient times,the spread of disease seriously threatens the healthy life of human beings.With the development of technology,the accelerated pace of life,crowd concentration and other factors,for the current spread of the disease to create a better environmental conditions.Therefore,analysis on communication mechanism and the study of disease prediction and effective control is of great significance to the spread of disease.However,the traditional analysis of disease spreading is assumed to be in a homogeneous groups,and there is no good description of the reality of society in the transmission process of heterogeneous groups.With the study of complex networks,while the network edges represent the interactions among the individuals in the crowd spread,can further refine the disease propagation mechanism and has more practical significance.In this paper,based on the study of single disease spreading dynamics,multi-layer complex network model is used to study disease spreading with the effect of external media information,individual behavioral habits,and the interaction diseases spreading situations.At the same time,the dynamics of disease spreading was analyzed by using Markov chain,mean field theory and Monte Carlo simulation.The theoretical analysis and computer simulation provides a better prospect for the effective control of disease transmission in multiple environments.Firstly,the effect of external media information on disease transmission was studied.Based on the multi-layer complex network transition model.The upper layer of the network described as UAU spreading mechanism,is a virtual network layer that shows information such as disease prevention information obtained from social software or mass media.Unaware state transitions from ordinary individuals without prevention information For Aware state with disease prevention information.The lower layer of the network described as SIS transition mechanism,which indicates the individual’s physical contact with the disease source or patient in our real life,and then from the Susceptible status to the Infected.Because people’s virtual contact in life is not the same physical contact with the actual,so the links of two networks are different.But nodes in the complex network are representing the same individual.Using the mean field theory,the thresholds of disease transmission were analyzed.The results indicated that the prevention of disease in the process of disease transmission much significant in control.Secondly,the effect of external media information and individual behavior on the spread of disease was studied.This paper further considered the behavior of individuals,more in line with the social nature of the crowd and avoid disadvantages of behavior factors.Through the multi-layer network model,the active and inactive states are introduced.Individuals in the active state will keep all links with the surrounding neighbors,while the inactive individuals can only guarantee the link with the active state individuals.Changes in this link have had an impact on the process of information and disease transmission.Based on heterogeneous mean-field approach,we analyze the epidemic thresholds of the two diseases and compute the temporal evolution characterizing the spreading dynamics which gives a new visual direction for disease transmission under individual behavior and outside information.Thirdly,the effect of disease interaction on disease transmission was studied.The use of external information and disease spread of the multi-layer network model,the information layer into another category of diseases.The two diseases are simultaneously spreading in one group,through the host influence each other’s transmission process.From the theoretical analysis of the transmission threshold,the results show that the spread of a disease by the extent of the outbreak of another disease.With the computer simulation,we focused on the reciprocal promotion of disease and the mutual damage between diseases.Finally,the effect of individual behavior on the spread of disease was studied.According to complex network model,the previously mentioned individual activity was introduced.In the same way,the disease propagation thresholds in this scenario are deduced and validated by computer simulation.The results showed that individual activity played an important role in delaying and accelerating the spread of disease in the interaction scenario,which paved the way for the further study of disease interaction and individual behavior.In summary,this paper makes good use of the multi-layer complex network model to study the real-life disease in different situations of the spread of the problem.This model can be applied to the similar communication system in life,and it can find the best management strategy and guarantee the utility of communication management maximization.Keywords public opinion control,advertisement marketing,Internet plus and so on.