We generated simulated disease outbreaks that spread based on the zip code adjacency graph with additional edges added to simulate travel patterns and injected these outbreaks into real-world hospital data. We evaluated detection time and spatial accuracy using the learned graphs for these simulated injects (Fig. 1 ). This figure also shows the detection performance given the true (adjacency plus travel) graph, the adjacency graph without travel patterns and the average performance given randomly generated graphs. We observe that the learned graph achieves comparable spatial accuracy to the true graph, while the adjacency graph has lower accuracy. Additionally, the learned graph is able to detect outbreaks over a day earlier than the true graph and 1.5 days earlier than the adjacency graph. Thus, our method can successfully learn the additional edges due to travel patterns, substantially improving detection performance.