Often individuals prevent infection by avoiding unprotected contact with infected once they know the state of their contacts or are aware of the potential risks of such infection [12] [13] [14] [15] [16] [17] [18] [19] [20] 28, 32] : such is the case of many sexually transmitted diseases [12, [41] [42] [43] , for example, and, more recently, the voluntary use of face masks and the associated campaigns adopted by local authorities in response to the SARS outbreak [11, [13] [14] [15] . In the present study, we assume that individuals are not centrally supervised or informed: Individual decision is based on available local information about the health state of one's contacts. To the extent that such information spreads quickly and contacts are not too frequent, one can study analytically the limit in which the network dynamics -resulting from adaptation to the flow of local information -is much faster than disease dynamics. In this case, one may separate the time scales between network adaptation and contact (disease) dynamics: The network has time to reach a steady state before the next contact takes place. Consequently, the probability of having an infected neighbor is modified by a neighborhood structure which will change in time depending on the impact of the disease in the population and the overall efficiency of local information flow. It will be shown that a quickly adapting community induces profound changes in the dynamics of disease spreading, irrespective of the underlying epidemic model. Furthermore, we will demonstrate numerically that the two limiting cases amenable to analytic treatment -static networks on the one hand, and quickly adapting networks on the other hand