Impacts of Information Propagation on Individual Migration Routes and Epidemic Spread Over Metapopulation Networks

Abstract

Information propagation driven by the epidemic may cause the awareness of individuals to change their behavior, thus preventing themselves from being infected. For example, the aware individuals migrate away from areas with severe infection. In this paper, we study the coupling transmission of epidemic and information in metapopulation networks, and mainly explore how the change of individual migration behavior affects the epidemic spreading. Combined with the transition probability tree of individual states, we use Markov chain approach for theoretical analysis and derive the epidemic threshold. Through numerous Monte Carlo simulation, we verify the accuracy of Markov equations for the prediction of epidemic sprading. The results show that the role of information transmission in suppressing the epidemic in terms of the epidemic threshold and the infection scale is very limited. Further increase of information transmission rate beyond its critical value will no longer affect the epidemic. The initial population distribution is a fundamental factor in the epidemic dynamics, and in the case of heterogeneous distribution, an appropriate movement of individuals can delay the epidemic spread with a smaller threshold. In addition, topological homogeneity of individual migration route is beneficial for the epidemic control. This study analyzes the interaction between epidemic and information on the metapopulation network model, which can provide guidance for epidemic intervention in reality.