The Study Of Propagation Dynamics Of Infectious Diseases In Complex Networks

Human health is constantly threatened by disease,certain infectious diseases may even form large-scale outbreaks within a few days,which have huge effects on people's lifestyles,the physical conditions of family members,and the uses of social resources.In the field of disease transmission,many experts and scholars study the scale of disease transmission and the outbreak threshold,mainly focusing on the methods of using theoretical analysis and combining disease transmission dynamics with behavioral dynamics to build models.For infectious diseases that can be prevented by vaccination,how to improve the vaccination rate to prevent and control infectious diseases has become an important issue for scholars in related fields.Based on basic modeling methods of complex networks and the perspective of vaccination behavior decision of individuals,this thesis explores the impact of memory and social influence respectively on individuals' vaccination behavior and the process of disease transmission.Firstly,this thesis mainly relies on the vaccination-transmission model to explore the influence of memory factor on individuals' inoculation behavior and the scale of disease transmission.The memory model mentioned here is to introduce the influence of an individual's payoff from last round into the calculation of her fitness in the specific environment.The simulation results show that memory information can inhibit individuals' desire for vaccination in the specific environment.In detail,when the historical payoff account for a higher proportion of fitness,the lower the vaccination rate of the entire population,and the larger the scale of infected individuals.The reason is that in the heterogeneous network,as the memory strength or the proportion of historical payoff information increases,the hub nodes are unwilling to be vaccinated,and these hub nodes can easily spread their strategies to small nodes,resulting in the decreases of vaccination rate in the entire population.Secondly,this thesis explores the effect of social influence factor on individuals' vaccination behavior and disease transmission process.Applying similar research methods,this thesis introduce a part of individuals who consider the strategies and payoffs of all interactive objects in the vaccination decision-making process to verify the impact of such individuals on the vaccination coverage and the scale of disease transmission in the population.The simulation results show that in the lattice network and the random network,social influence has a promoting effect on individuals' vaccination behavior and suppression of disease outbreaks;while in the Barabási-Albert scale-free network,the role of social influence has two sides.That is,there is a critical value for the cost of vaccination,when the vaccination cost is less than the critical value,social influence factor inhibits individuals' choice of vaccination;when the vaccination cost is greater than the critical value,social influence factor can instead promote individuals' choice of vaccination.In this thesis,these results are attributed to the different network topologies.Compared with previous studies,the method of this thesis focuses on the extension of the inoculation-transmission model,which specifically considers the impact of the differences of individual psychology in the decision-making process.From the perspective of reality,there are still many similar effects worthy of being studied,but it is difficult to measure the psychological driving force through statistical modeling.Even so,we still hope that our research can provide ideas for related research in this field,and we also hope it has a positive impact on how to increase the vaccination coverage in the population to prevent the spread of disease in the public health field.