CalCORVID: A Dynamic RShiny Dashboard Approach to Visualize Spatiotemporal Clusters for Public Health Surveillance
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Background
Infectious disease surveillance is an essential component of public health for preventing and mitigating outbreaks. Systematically applying statistical methods for anomaly detection to surveillance data can expedite outbreak response through early warning. A commonly used approach is the usage of spatiotemporal scan statistics as implemented in SaTScan, a software that analyzes spatiotemporal data to identify clusters of events over space and time that deviate from expected values. Some health departments identify outbreaks and prioritize resources using SaTScan for early cluster detection for diseases such as salmonellosis, legionellosis, and COVID-19. However, as a standalone software, SaTScan v10.2.1 does not provide functionality to easily disseminate visual cluster results over time in a way that is tailored to epidemiologists’ needs for real-time disease surveillance.
Results
We developed an open source dashboard that provides a customizable framework for displaying results and facilitating the use of SaTScan for public health surveillance. The California Clustering for Operational Real-time Visualization of Infectious Diseases (CalCORVID) dashboard is built using RShiny, is specifically designed for SaTScan outputs, and can be easily adapted to display any jurisdiction’s results. This dashboard features a map and corresponding results table, the option to view historical results, integration of the Social Vulnerability Index (SVI) to contextualize clusters, and interactive elements to enhance usability for epidemiologists.
Conclusions
We present CalCORVID as a complementary tool to native outputs of SaTScan v10.2.1, allowing users to visualize, customize, and distribute their results for specific public health use cases. Epidemiologists currently using SaTScan can adapt the provided code repository and dashboard template to display their own jurisdictions’ results, facilitating dissemination of cluster results for real-time, ongoing disease surveillance.
