Graph Structure Learning from Unlabeled Data for Early Outbreak Detection

Processes such as disease propagation and information diffusion often spread over some latent network structure that must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (such as a disease outbreak), the authors aim to learn a graph structure that can be used to accurately detect future events of that type. They propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subsets detected with and without the graph constraints. Their framework uses the mean normalized log-likelihood ratio score to measure the quality of a graph structure, and it efficiently searches for the highest-scoring graph structure. Using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, the authors show that their method learns a structure similar to the true underlying graph, but enables faster and more accurate detection.

Authors

• Sriram Somanchi
• Daniel B. Neill New York University

Keywords

• Training
• Image edge detection
• Diseases
• Event detection
• Public healthcare
• Intelligent systems
• Surveillance
• Artificial intelligence
• Graph Structure
• Unlabeled Data
• Structure Learning
• Outbreak Detection
• Disease Transmission
• Disease Outbreaks
• Detection Performance
• Learning Framework
• Dissemination Of Information
• Detection Time
• Nodes In The Graph
• Training Examples
• Likelihood Ratio Statistic
• Labeled Training Data
• Subset Of Nodes
• Graph Learning
• Improve Detection Performance
• Emergency Department Data
• Underlying Graph
• Adjacency Graph
• Additional Edges
• Overlap Coefficient
• Travel Patterns
• Spatial Accuracy
• Edge Removal
• Daily Scores
• Spatial Coefficient
• Detection Power
• Random Edge
• disease surveillance
• spatial scan statistic