Epidemic spreading on weighted networks with adaptive topology based on infective information

• A new epidemic spreading model considering the interaction between network topology and epidemic spreading is established.
• The network topology varies according to the global and local infective information of individuals.
• The influences of the interaction on epidemic spreading and network topology are analyzed by discrete-time Monte-Carlo simulation.

In this paper, we consider epidemic spreading on a weighted adaptive network in which the network topology varies according to the global and local infective information of individuals. We focus on the relationship between network topology and epidemic spreading. Interacting strength is defined to evaluate the level of how individuals’ infective information taking effects on their connections. The model is analyzed by discrete-time Monte-Carlo simulations with an initial BA scale-free network. It is found that greater interacting strength leads to higher epidemic threshold, lower average disease density of steady-state and shorter epidemic prevalent decay time. Besides, the interaction tends to change the initial BA scale-free network to a fat-tail network while the scaling exponent almost keeps unchanged. In addition, individual who reacts with local infective information will significantly restrain the outbreak of disease comparing to the one with global infective information, and this phenomenon becomes more notable if the interacting strength becomes greater.