Effect of local rewiring in adaptive epidemic ERDOS-RENYI random networks

Adaptive epidemic network is driven by two main processes, (1) infectionrecovery process that changes the states of the nodes, and (2) rewiring process that modifies the topology of the network. In the past two decades, epidemic models on adaptive networks have gained interests because understanding the dynamics between these two processes can be key to improving control of diseases. However, in most of these models, the rewiring mechanism is based on information known globally, i.e., everyone knows the health status of all others in the network. This concept is not practical in real life for large network. This dissertation aims to provide a more realistic rewiring model for epidemic-control strategy. We propose a method where the decision of an individual is based on its local information. We call the original rewiring method global rewiring and ours local rewiring. In this dissertation, we investigate a susceptible-infected-susceptible (SIS) epidemic model on adaptive networks for global and local rewirings. Here we find that local rewiring networks have less chance to prevent disease spreading than global rewiring networks because of limited local information. However, using kinetic Monte Carlo simulations, our results show that there are phase overlaps between both rewiring methods. This means that under a certain circumstance, even with limited local information, we can predict outcomes of an epidemic for systematic interventions.