A clustering environment for real-time tracking and analysis of Covid-19 case clusters

Spatial surveillance systems can be an effective and efficient way to provide early spatio-temporal warning signals of infectious diseases outbreaks. The development of spatial surveillance systems that can handle near-real time hospital spatial data have proven critical for directing intervention strategies and for risk mitigation during the ongoing monitoring and response to COVID-19. GeoMEDD, a geographic monitoring tool, was developed for such near-real time assessment of emergent localized disease. As the response to COVID-19 changed, moving through phases such as increased scientific understanding, testing variability, vaccine availability and uptake, and new variants, so GeoMEDD has also evolved. Here we present two advances for GeoMEDD; a fully automated cluster environment with a spatial database at its heart and a cluster tracking module to classify clusters based on the transition state of the cluster lifecycle. Our detailed use case analysis shows that these advances have improved local and global cluster analysis, contextual information dissemination, monitoring emergence based on underlying spatial structure and cluster evolution analysis. We believe that the addition of the fully automated cluster environment to GeoMEDD would be particularly beneficial for health institutions as well as governmental health organizations for disease outbreak detection due to the efficiency in data ingestion and analysis, while the addition of the cluster tracking module will advance research into the mechanics behind disease diffusion in space and time.