As a result, quite a few studies have recently investigated the impact of dynamical networks on disease progression, as well as the influence of the way information (disease awareness) flows in parallel with disease progression and the role of noise in disease dynamics [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] . At par with these models, our model studies disease dynamics in a finite and constant population of individuals. Contrary to current models though (see Discussion Section for more details), our dynamical contact structure allows for a variable number of overall links between individuals, which in turn depends on the overall incidence of disease in the population. This increased complexity of the model, however, will allow us to describe analytically disease dynamics in finite populations. Infection occurs along the links of a contact network whose structure may change based on each individual's health status and the availability of information regarding the health status of others. We assume the existence of some form of (unsupervised) local information about the health status of social contacts, depending on which individuals may be more or less effective in avoiding contact with those infected while remaining in touch with the healthy. We investigate some of the most widely used models of disease spreading, such as the popular Susceptible-Infected-Susceptible (SIS) model [1, 3] , the Susceptible-Infected (SI) model [1] used to study, e.g., AIDS [1, 34] , and the Susceptible-Infected-Recovered (SIR) model [1, 35] , more appropriate to model, for instance, single season flu outbreaks [1] or computer virus spreading [5] . We provide analytical results which show that, on finite adaptive networks, the effective infectiousness of a disease depends on i) the size of the population, ii) the number of infected and ultimately iii) on the efficiency of local information spreading. These results, obtained under the assumption that network reshaping occurs much faster than disease spreading, are shown (numerically) to remain valid for a much wider range of time scales. When combined with the analytical results already known for static networks [3] , which describe well the opposite limit in which the network dynamics is slow compared to disease dynamics, we demonstrate that one can describe analytically most of the time scale spectra. We further show how availability of information may either drastically reduce the time required to eradicate a disease (when recovery from the disease is possible), or drastically increase the time after which the entire population becomes infected (when the disease cannot be cured). Finally, we discuss the implications of assuming that individuals exhibit diverse response profiles to available information [36] [37] [38] .