The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic

Kolak, Marynia; Li, Xun; Lin, Qinyun; Wang, Ryan; Menghaney, Moksha; Yang, Stephanie; Anguiano, Vidal

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data‐driven knowledge discovery and more sophisticated spatial analysis central to the COVID‐19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county‐level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high‐quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user‐centered design to ground the direction and design of Atlas application development.