where i denotes the number of infected individuals in the population and r the number of recovered (immune). The rescaling parameter g for the SI and SIS models is the same, but with r~0. Note that g scales linearly with the frequency of infected in the population, decreasing the more individuals get infected (assuming w SS =w SI w1), and depends implicitly (via the ratio w SS =w SI ) on the amount of information available. We would like to stress the distinction between the description of the disease dynamics at the local level and that at the population level. Strictly speaking, a dynamical network does not change the disease dynamics at the local level, meaning that infected individuals pass the disease to their neighbors with probability intrinsic to the disease itself. At the population level, on the other hand, disease progression proceeds as if the infectiousness of the disease effectively changes, as a result of the network dynamics. Hence, analyzing an adaptive network scenario at a population level can be achieved via a correction on the transmission probability, keeping the mathematically more attractive wellmixed scenario. In this sense, from a well-mixed perspective, dynamical networks contribute to change the effective infectiousness of the disease, which becomes frequency and information dependent.