Respiratory viruses are highly infectious and cause acute illness in millions of people every year. However, there is wide variation in the physiologic response to exposure at the individual level. Some people that are exposed to virus are able to completely avoid infection. Others contract virus but are able to fight it off without exhibiting any symptoms of illness such as coughing, sneezing, sore throat or fever. It is not well understood what characteristics may protect individuals from respiratory viral infection. These individual responses are likely influenced by multiple processes including both the basal state of the human host upon exposure and the dynamics of host immune response in the early hours immediately following exposure. Many of these processes play out in the peripheral blood through activation and recruitment of circulating immune cells. Global gene expression patterns measured in peripheral blood at the time of symptom onset – several days after viral exposure – are highly correlated with symptomatic manifestation of illness. However, these later-stage observations do not necessarily reflect the spectrum of early time point immune processes that predict eventual infection. Because signal amplitude is small at these early time points, the ability to detect early predictors of viral response has not yet been possible in any individual study. By combining data collected across 7 studies and leveraging the state-of-the-art in analytical algorithms, this Challenge aims to develop early predictors of susceptibility and contagiousness based on expression profiles that were collected prior to and at early time points prior to, and following viral exposure.