Finally, it is also worth mentioning that the knowledge about the health state of the others may not always be accurate or available in time. This is for instance the case for diseases where recently infected individuals remain asymptomatic for a substantial period. The longer the incubation period associated with the disease, the less successful individuals will be in escaping infection, which reduces in our model to a lower effective rate of breaking SI links, with the above mentioned consequences. Moreover, the (social) network through which awareness of the health status of others proceeds may lead to different rates of information spread. In such cases, one may model explicitly the spread of the health state of each individual, as done in Refs. [28, 32] , and study the interplay between disease expansion and individuals' awareness of the disease. Of course, depending on the particular disease at hand and the contact network along which it propagates, one may have to take other factors into account, besides network adaptability, in order to make accurate predictions of disease progression. Creation and destruction of links may for instance not always occur randomly, as we assumed here, but in a way that is biased by a variety of factors such as social and genetic distance, geographical proximity, family ties, etc. The resulting contact network may therefore become organized in a specific way, promoting the formation of particular structures, such as networks characterized by long-tailed degree distributions or with strong topological correlations among nodes [2, [46] [47] [48] which, in turn, may influence the disease dynamics. The impact of combining such effects, resulting from specific disease scenarios, with those reported here will depend on their prevalence. A small fraction of non-random links, or of ties which cannot be broken, will likely induce small modifications on the average connectivity of the contact network, which can be incorporated in our analytic expressions without compromising their validity regarding population wide dynamics. On the other hand, when the contact network is highly heterogeneous (e.g., exhibiting pervasive longtail degree distributions), non-random events may have very distinct effects, from being almost irrelevant (and hence can be ignored) to inducing hierarchical cascades of infection [49] , in which case our results will not apply.