  In the last decades, many authors have used the
susceptible-infected-recovered model to study the impact of the disease
spreading on the evolution of the infected individuals. However, few authors
focused on the temporal unfolding of the susceptible individuals. In this
paper, we study the dynamic of the susceptible-infected-recovered model in an
adaptive network that mimics the transitory deactivation of permanent social
contacts, such as friendship and work-ship ties. Using an edge-based
compartmental model and percolation theory, we obtain the evolution equations
for the fraction susceptible individuals in the susceptible biggest component.
In particular, we focus on how the individual's behavior impacts on the
dilution of the susceptible network. We show that, as a consequence, the
spreading of the disease slows down, protecting the biggest susceptible cluster
by increasing the critical time at which the giant susceptible component is
destroyed. Our theoretical results are fully supported by extensive
simulations.
