Disease spread on transport networks of U.S. cattle

Large-scale geographic predictions of disease spread are rare. European livestock disease models are data-rich case studies but are limited to relatively small, country-specific scales.  Generalizing to larger systems, such as the European Union or United States, is difficult with insufficient spatial resolution and alignment among data sets to capture inherently complex contact networks. By integrating novel movement data, network scaling advances, and disease models that absorb location, disease spread, and other uncertainties to predict disease impact in US cattle, we illustrate the potential benefits of modelling for policy in complex, data-poor systems.