Our respondents began filling out the survey on the day that WHO upgraded the pandemic threat category of the H1N1 outbreak from 4 to 5 and spans the week in which the number of WHO-confirmed cases increased tenfold. While our sampling design is subject to many of the usual criticisms of internet-based surveys and is not necessarily representative of the general population, the unparalleled immediacy, longitudinal nature, and the large number of respondents it contains make our data set unique and scientifically important for the study of the spread of information and distribution of risk perception and behavioral change during the most uncertain time (i.e. the initial phase) of an epidemic of a virus novel to the human population.  Accurate noise projection for reduced stochastic epidemic models We consider a stochastic susceptible-exposed-infected-recovered (SEIR) epidemiological model. Through the use of a normal form coordinate transform, we are able to analytically derive the stochastic center manifold along with the associated, reduced set of stochastic evolution equations. The transformation correctly projects both the dynamics and the noise onto the center manifold. Therefore, the solution of this reduced stochastic dynamical system yields excellent agreement, both in amplitude and phase, with the solution of the original stochastic system for a temporal scale that is orders of magnitude longer than the typical relaxation time. This new method allows for improved time series prediction of the number of infectious cases when modeling the spread of disease in a population. Numerical solutions of the fluctuations of the SEIR model are considered in the infinite population limit using a Langevin equation approach, as well as in a finite population simulated as a Markov process. The interaction between deterministic and stochastic effects in population dynamics has played, and continues to play, an important role in the modeling of infectious diseases. The mechanistic modeling side of population dynamics is well known and established. 1, 2 These models typically are assumed to be useful for infinitely large, homogeneous populations, and arise from the mean field analysis of probabilistic models. On the other hand, when one considers finite populations, random interactions give rise to internal noise effects, which may introduce new dynamics. Stochastic effects are quite prominent in finite populations, which can range from ecological dynamics 3 to childhood epidemics in cities. 4, 5 For homogeneous populations with seasonal forcing, noise also comes into play in the prediction of large outbreaks. [6] [7] [8] Specifically, external random perturbations change the probabilistic prediction of epidemic outbreaks as well as its control. 9 When geometric structure is applied to the population, the interactions are modeled as a network. 10, 11 Many types of static networks which support epidemics have been considered. Some examples include small world networks, 12 hierarchical networks, 13 and transportation networks of patch models. 14 In addition, the fluctuation of epidemics on adaptive networks, where the wiring between nodes changes in response to the node information, has been examined. 15 In adaptive network models, even the mean field can be high dimensional, since nodes and links evolve in time and must be approximated as an additional set of ordinary differential equations.