Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes Forecasting how an epidemic will unfold, its trajectory and eventual size, usually presumes access to the full contact network. In practice, no one ever has the full mapping of networks. New research shows that full mapping is unnecessary. By observing only the local neighborhoods around a small number of individuals, the kind of partial data that can actually be collected during an epidemic, the authors prove that global outcomes, such as the eventual size of an epidemic, can be predicted with rigorous accuracy guarantees. The work formally connects what public health planners can observe locally—for example, connections revealed by contact tracing and similar methods—to how an epidemic behaves globally, giving them a principled way to turn limited, realistic data into reliable forecasts.