Greece; Sulev Kõks, University of Tartu, Estonia; Frank Adaptive Contact Networks Change Effective Disease Infectiousness and Dynamics Human societies are organized in complex webs that are constantly reshaped by a social dynamic which is influenced by the information individuals have about others. Similarly, epidemic spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to avoid contact with those infected and to remain in touch with the healthy. Here we study disease dynamics in finite populations in which infection occurs along the links of a dynamical contact network whose reshaping may be biased based on each individual's health status. We adopt some of the most widely used epidemiological models, investigating the impact of the reshaping of the contact network on the disease dynamics. We derive analytical results in the limit where network reshaping occurs much faster than disease spreading and demonstrate numerically that this limit extends to a much wider range of time scales than one might anticipate. Specifically, we show that from a population-level description, disease propagation in a quickly adapting network can be formulated equivalently as disease spreading on a well-mixed population but with a rescaled infectiousness. We find that for all models studied here – SI, SIS and SIR – the effective infectiousness of a disease depends on the population size, the number of infected in the population, and the capacity of healthy individuals to sever contacts with the infected. Importantly, we indicate how the use of available information hinders disease progression, either by reducing the average time required to eradicate a disease (in case recovery is possible), or by increasing the average time needed for a disease to spread to the entire population (in case recovery or immunity is impossible). During recent years it has become clear that disease spreading [1] [2] [3] transcends geography: the contact process is no longer restricted to the immediate geographical neighbors, but exhibits the stereotypical small-world phenomenon with inescapable impact [4] [5] [6] [7] . The SARS and, at the time of writing, the influenza A outbreaks provide arresting examples of this effect in a biological context, although the spread of computer viruses constitutes perhaps the most obvious manifestation of such a property. Recent advances in the science of networks [2] also brought the impact of disease interactions beyond geography to the spotlight, providing compelling evidence of the role that the networks of contacts between individuals or computers play in the dynamics of infectious diseases [3, 5] . In the majority of cases in which complex networks of disease spreading have been considered [7] , they were taken to be static entities. However, social networks are intrinsically dynamical entities. In fact, modern societies have developed rapid means of information dissemination, both at local and at centralized levels, which one naturally expects to alter individuals' response to vaccination policies, their behavior with respect to other individuals and their perception of likelihood and risk of infection [8] . In some cases one may even witness the adoption of centralized measures, such as travel restrictions [9, 10] or the imposition of quarantine spanning parts of the population [11] , which may induce abrupt dynamical features onto the structure of the contact network. Furthermore, individual knowledge (based on local information) about the health status of acquaintances, partners, relatives, etc., combined with individual preventive strategies [12] [13] [14] [15] [16] [17] [18] [19] [20] such as condoms, vaccination, the use of face masks or prophylactic drugs, avoidance of visiting specific web-pages, staying away from public places, etc., may also lead to bias in the organization of disease paths along dynamical networks.