Impact Of Heterogeneous Population On Disease Spreading In Activity-driven Networks

The current SARS-CoV-2 pandemic emphasizes infectious diseases remain a threat to human life.This paper explores the disease transmission patterns through a network epidemiological dynamics model.In human society,individual interactions intrinsically change,with profound implications for epidemics.The activity-driven model,a type of temporal network,offers an excellent framework to study epidemic processes in dynamical interaction.Based on the epidemic spread model,this paper focuses on exploring the epidemic transmission that is impacted by individual heterogeneous social attitudes and contacts on the activity-driven network.In this paper,we first study how social attitudes affect the transmission of infectious diseases in activity-driven networks.Here,we divide a population into ”riskignorant” and ”risk-averse”,in which risk-averse individuals will reduce their social intensity(Social intensity refers to the number of social contacts in the social process)and risk-ignorant individuals will not.A parameter p controls the proportion of riskaverse individuals,and therefore risk-ignorant individuals by 1-p.With the aid of mean-field theory,we calculate epidemic thresholds,as well as validate theoretical predictions with extensive Monte Carlo simulations.It is shown numerically and theoretically that reducing the social intensity and increasing the number of risk-averse individuals are effective ways of controlling epidemic outbreaks.An appropriate proportion of the risk-averse individual will lead to an epidemic die-out,which is based on a small spreading rate.Second,we introduce heterogeneous contacts of individuals in the activity-driven network,where the number of individual contacts is obtained from uniform,powerlaw,and poisson distribution sampling,respectively.Theoretical thresholds for epidemic outbreaks are derived and simulation experiments are carried out with the help of the individual-based mean-field method and the Monte Carlo method,respectively.The results indicate that weak heterogeneous contacts of individuals suppress epidemic outbreaks and reduce the epidemic size when the number of individual contacts follows a poisson distribution.In contrast,weak heterogeneous contacts of individuals under uniform and power-law distributions have less influence on the process of the epidemic.Our research provides a new perspective for understanding the effect of individuals with different social attitudes and heterogeneous contacts of individuals in the process of an epidemic.