Epidemic propagation on adaptive coevolutionary networks with preferential local-world reconnecting strategy

In the propagation of epidemic in population, individuals adaptively adjust their behaviors to avoid the risk of epidemic. Different from the existing studies where new links are established randomly, a local link is established preferentially in this paper. We propose a new preferentially reconnecting edge strategy depending on spatial distance (PR-SD). For the PR-SD strategy, the new link is established at random with probability p and in a shortest distance with the probability 1-p. We establish the epidemic model on adaptive network using Cellular Automata, and demonstrate the effectiveness of the proposed model by numerical simulations. The results show that the smaller the value of parameter p, the more difficult the epidemic spread is. The PR-SD strategy breaks long-range links and establishes short-range links as many as possible, which causes the network efficiency to decrease quickly and the propagation of epidemic is restrained effectively.