Development of a Novel Spatiotemporal Anomaly Detection Algorithm for Count Data

Music City Center 

Although prospective anomaly detection in multidimensional count data is an important area of research for multiple fields, including disease outbreak detection, the ability to quickly identify potential anomalies using real-time data is not commonly available. Spatiotemporal data are commonplace in disease surveillance, as well as diverse fields including econometrics, and environmental science. Increasingly, these data are available in near real-time. This paper presents a new algorithm for the rapid identification of anomalous, discrete datapoints. Temporal graph signal decomposition (TGSD) is first applied to identify and remove periodic spatial and temporal components from the data. Space-time autoregressive integrated moving average (STARIMA) models are then iteratively fitted to the detrended data with observations flagged in real-time after comparison to the fitted model's predictive window. This approach is demonstrated on simulated disease surveillance data, with a panel of injected outbreak scenarios. Each scenario is simulated 10,000 times, with performance assessed via sensitivity, specificity, and timeliness of detection.

Anomaly detection

Time series

Data mining

Disease surveillance 

Section on Statistics in Epidemiology