Research On The Strategy Updating Mechanism Of Vaccination In Complex Networks

When infectious diseases spread in complicated social networks,voluntary vaccination plays an essential role in achieving widespread immunity to them.When an individual voluntarily vaccinates,the individual weighs the risk of the infection and the cost of vaccination to make vaccinating decisions.When the season of transmission of an infectious disease is over,individuals update strategies based on the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases of last infection season to make vaccinating decisions on next infection season.Studies have shown that some effective vaccination strategies,such as imitation,learning and pairwise comparison,can increase the level of inoculation in complex network as the disease spreads.However,these strategy-updating ways are made based on social learning from their successful neighbors and their past experiences before the seasonal epidemic begins.,and there are some shortcomings.First,individuals focus on the latest gain rather than the potential gain of the strategy when they determine whether adopt the strategies based on the gain of last season by comparing the neighbor.Also,some update strategies are affected by the selection strength.Distinct strength may affect the rationality of individuals when determining update strategies.Besides,because the spread of a disease is a highly stochastic process,a few past experiences are not well suited as the primary basis for future decision-making.Moreover,in most cases,when an infectious disease reemerges,its nature are often accompanied by mutations.As a result,past vaccination and infection experiences do not apply to the present epidemic situation.In addition,When infectious diseases spread in complicated social networks,distinct strategy-updating ways have different effects on voluntary vaccination behaviors.Based on this,this paper proposes a new policy updating mechanism(referred to as " SVIL ")that considers the risk of locally mixed networks.At the end of each season of infectious diseases,individuals in locally mixed networks predict their risk of infection and update their probability of vaccination on the next season.To reflect the willingness of individuals to voluntarily vaccinate,individuals adopt a mixed vaccinating strategy rather than a pure strategy.Through the simulation experiments in complex networks,we investigate the impact of the strategy updating mechanism on the voluntary vaccination,and also studied the influence of the infection force,network structure,average degree and network structure of " neighbors of neighbors " on the voluntary vaccination under the strategy updating mechanism.The study found that the newly proposed SVIL is more consistent with the willingness of individuals to voluntarily vaccinate in real life,but also improve the vaccination level,inhibit the spread of the disease.In addition,when infectious diseases spread in complicated social networks,which strategy updating mechanism can increase the vaccination level of the entire population and control the spread of the epidemic under voluntary vaccination.In this paper,based on the proposed SVIL,we proposes another new policy updating mechanism(referred to as "SBIL")that considers the behavior of locally mixed networks.We compare the effects of the newly proposed SVIL,SBIL and the classic strategy updating mechanism such as Fermi equation learning strategy and imitation strategy updating mechanism to voluntary vaccination behaviors.By comparing the vaccination level,the final epidemic size and the total social cost when individuals adopt different ways,we verify that SVIL in this paper can promote individual vaccination and inhibit the spread of infectious diseases in complex networks compared with the classic strategy updating mechanism.