Spatio-temporal data monitoring using statistical process control charts

Music City Center 

Spatio-temporal process monitoring (STPM) has garnered significant attention recently due to its wide range of applications, including environmental monitoring, disease surveillance, and streaming image processing. Monitoring spatial data streams presents unique challenges, as these data often exhibit complex structures, such as latent spatio-temporal correlations, intricate spatio-temporal mean patterns, and nonparametric distributions. As a result, STPM is a challenging research problem. In practice, when a spatial process experiences a distributional shift (e.g., a mean shift) at a specific time, it is critical to detect this shift promptly, as it often signals a structural change in the process (e.g., a disease outbreak). Statistical process control charts are a key analytic tool for addressing sequential decision-making problems like these. However, traditional control charts are not well-suited to STPM due to the inherent complexity of spatial data streams. In this talk, we will explore recent advancements in control charts developed specifically for STPM and discuss their applications in infectious disease surveillance.

Statistical process control

Disease surveillance

Environmental monitoring