| Throughout human history,every epidemic of infectious diseases has seriously threatened the safety of human life and the development of society and economy,such as SARS and Ebola virus.COVID-19 is still spreading around the world.Therefore,how to accurately simulate the spread of infectious diseases in the population and how to control the spread of emergent infectious diseases is one of the most urgent problems in the world today.At the same time,with the rapid development of mobile communication technology,news related to infectious diseases also spreads rapidly on social networks or online social networks.Many scholars have studied the inhibitory effect of news on infectious diseases.But in the age of information explosion,it is difficult to guarantee the accuracy of messages.While much of the information shared on social media is well-meaning and reliable,misinformation is also prevalent,and many of it contributes to the spread of infectious diseases.Therefore,this thesis divides the message into information and misinformation,explores the dynamic process of infectious disease transmission under the influence of the two kinds of message competitive transmission,and further discusses how to use information to intervene the spread of misinformation and infectious disease.（1）A double-layer coupled network model,U6)U-SIR,is proposed to explore the impact of the competitive transmission of information and misinformation on the spread of infectious diseases.The upper network represents the virtual network of messages competitive transmission,while the lower network is the physical network of infectious disease transmission.The nodes of the two layers correspond one by one,and the message layer network is the extension of the disease layer network.In the message layer,U6)U model is adopted and group cognition level is introduced to describe the competitive propagation of two kinds of messages.In the disease layer,the classic SIR model is used to describe the spread of infectious diseases.The Micro-Markov Chain Approach（MMCA）is used to analyze the model and calculate the transmission threshold of infectious disease.At the same time,a large number of numerical simulations are carried out.The results show that the dissemination of information can inhibit the spread of infectious diseases,but the dissemination of misinformation can weaken the effect of information,and the group cognition level can affect the spread of infectious diseases by influencing the competitive dissemination of messages.（2）On the basis of the existing model,two global and local intervention mechanisms of mass media and domain experts are introduced.The M-U6)U-SIR model is established for mass media.The mass media refers to the official source of reliable news,which periodically disseminates information to all nodes in the message layer.It is an institution rather than a real individual,so it does not participate in the spread of infectious diseases.For the scenario of local intervention by domain experts,the U6)EU-SIR model is established,and E represents individuals with knowledge related to infectious diseases and who have been disseminating information.The two models were theoretically analyzed by using the Micro-Markov Chain Approach（MMCA）to obtain the disease transmission threshold,and extensive Monte Carlo（MC）simulation experiments were carried out.The results show that both mass media and domain experts can effectively control the spread of misinformation and infectious diseases,but the network structure will affect the effect of intervention,and different strategies should be adopted for different networks.In this study,a new coupled model of competitive transmission of messages and spread of infectious diseases is proposed,which deepens the understanding of the coupled transmission of messages and spread of infectious diseases,and further explores the effectiveness of two different interventions,providing some constructive guidance for promoting the management of messages transmission and the control of spread of infectious diseases in society. |
