Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refer...Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.展开更多
This paper proposes the SISRS epidemic model to represent alcohol addiction among people.The spreading of alcohol addiction is controlled by creating awareness among the people and also by treating them to overcome it...This paper proposes the SISRS epidemic model to represent alcohol addiction among people.The spreading of alcohol addiction is controlled by creating awareness among the people and also by treating them to overcome it.Multiplex network is used to study the dynamics of addiction.Alcoholism spreads over the physical contact layer and follows the SISRS process whereas human awareness spreads over the virtual contact layer and follows the UAU process.Based on the Microscopic Markov Chain Approach competing dynamics of spreading of alcohol addiction and human awareness diffusion are studied.Necessary conditions for the existence of an alcohol-free population are found.An optimal control problem using a suitable cost index is formulated to reduce the alcohol addicts and the optimal control strategy using Pontryagin’s Minimum Principle is determined.Numerical results are developed to find the effect of various parameters and to analyze the effects of different control strategies.The results obtained from this model are closer to the data collected in the National Survey of Drug Use and Health(NSDUH)from 2002 to 2018.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72171136 and 72134004)Humanities and Social Science Research Project,Ministry of Education of China(Grant No.21YJC630157)+1 种基金the Natural Science Foundation of Shandong Province(Grant No.ZR2022MG008)Shandong Provincial Colleges and Universities Youth Innovation Technology of China(Grant No.2022RW066)。
文摘Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.
文摘This paper proposes the SISRS epidemic model to represent alcohol addiction among people.The spreading of alcohol addiction is controlled by creating awareness among the people and also by treating them to overcome it.Multiplex network is used to study the dynamics of addiction.Alcoholism spreads over the physical contact layer and follows the SISRS process whereas human awareness spreads over the virtual contact layer and follows the UAU process.Based on the Microscopic Markov Chain Approach competing dynamics of spreading of alcohol addiction and human awareness diffusion are studied.Necessary conditions for the existence of an alcohol-free population are found.An optimal control problem using a suitable cost index is formulated to reduce the alcohol addicts and the optimal control strategy using Pontryagin’s Minimum Principle is determined.Numerical results are developed to find the effect of various parameters and to analyze the effects of different control strategies.The results obtained from this model are closer to the data collected in the National Survey of Drug Use and Health(NSDUH)from 2002 to 2018.
