Fast Graph Structure Learning from Unlabeled Data for Outbreak Detection

Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an anomalous subgraph which might be indicative of an emerging event. Typically, however, the network structure is unknown, and must be learned from unlabeled data, given only the time series of observed counts (e.g. daily hospital visits for each zip code).

Objective

Our goal is to learn the underlying network structure along which a disease outbreak might spread, and use the learned network to improve the timeliness and accuracy of outbreak detection.