Suppose all individuals seek to establish links at the same rate c. For simplicity, we assume that new links are established and removed randomly, a feature which may not always apply in real cases, where the limited social horizon of individuals or the nature of their social ties may constrain part of their neighborhood structure (see Discussion). Let us further assume that links may be broken off at different rates, based on the nature of the links and the information available about the neighbors: Let us denote these rates by b pq for links of type pq (p,q [ S,I,R f g). This allows us to write down a system of ordinary differential equations [39, 40] for the time evolution of the number of links of each type. These and all other technical details of the model are presented in the Methods section. In the steady state of the linking dynamics, the number of links of each type is given by L Ã pq~wpq L M pq , where w pq~c czb pq À Á are the fractions of active pq-links, compared to the maximum possible number of links L M pq , for a given number of S, I and R. In the absence of disease only SS links exist, and hence w SS determines the average connectivity of the network under disease free conditions, characterizing the type of the population under study. In the presence of I individuals, to the extent that S individuals manage to avoid contact with I, they succeed in escaping infection. Hence, the intuition is clear: Reshaping of the contact network based on available information of the health status of individuals will contribute to inhibit disease progression. In the extreme limit of perfect information and individual capacity to immediately break up contacts with infected, we are isolating all infected, thus containing disease progression. Our goal here, however, is to understand how and in which way partial individual information affects the overall disease dynamics.