Impacts of information propagation on epidemic spread over different migration routes

Information propagation driven by the epidemic may cause individuals awareness to change their behavior prevent themselves from being infected, as we observed in reality that aware often migrate away infected areas. In this work, study coupled interaction of spreading and information over a two-layer metapopulation network, where unaware separately take different migration routes, mainly explore how individual route affects spreading. Combined with transition probability tree states, use Markovian chain approach derive threshold for proposed model. Through numerous Monte Carlo simulations, verify accuracy equations prediction dynamics. The results show role transmission suppressing is limited. Further increase rate beyond some critical value will no longer affect epidemic. Detailed analysis has consider individuals, especially mobility frequency. addition, initial population distribution also fundamental factor dynamic. With heterogeneous distribution, frequent would delay spread, while homogeneous it does not. between separate routes provides helpful guidance intervention reality.