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Center for Nonlinear Studies |
Public health agencies use surveillance systems to detect and monitor diseases. These systems often rely on data sources that are chosen based on loose guidelines or out of convenience. Here, we present a new, data-driven method for designing surveillance systems. Our approach is a geographic optimization of data sources designed to achieve specific surveillance goals. The resulting networks outperformed existing systems when predicting seasonal and pandemic influenza hospitalizations