Epidemic outbreaks with adaptive prevention on complex networks

The adoption of prophylaxis attitudes, such as social isolation and use of face masks, to mitigate epidemic outbreaks strongly depends on the support of the population. In this work, we investigate a susceptible–infected–recovered (SIR) epidemic model, in which the epidemiological perception of the...
Ausführliche Beschreibung

The adoption of prophylaxis attitudes, such as social isolation and use of face masks, to mitigate epidemic outbreaks strongly depends on the support of the population. In this work, we investigate a susceptible–infected–recovered (SIR) epidemic model, in which the epidemiological perception of the environment can adapt the behavior of susceptible individuals towards preventive behavior. Two rules, depending on local and global epidemic prevalence, for the spread of the epidemic in heterogeneous networks are investigated. We present the results of both heterogeneous mean-field theory and stochastic simulations. The former does not predict a shift of the epidemic threshold, neither with global nor with local awareness. In simulations, however, local awareness can significantly raise the epidemic threshold, delay the peak of prevalence, and reduce the outbreak size. Interestingly, we observed that increasing the local perception rate leads to less individuals recruited to the protected state, but still enhances the effectiveness in mitigating the outbreak. We also report that network heterogeneity substantially reduces the efficacy of local awareness mechanisms since hubs, the super-spreaders of the SIR dynamics, are little responsive to epidemic environments in the low epidemic prevalence regime. Our results indicate that strategies that improve the perception of who is socially very active can improve the mitigation of epidemic outbreaks. Ausführliche Beschreibung