The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance

Abstract

We present CASE, an open-source framework for adaptive participatory research and disease surveillance. Unlike traditional survey platforms with static branching logic, CASE uses an event-driven architecture that adjusts survey workflows in real time based on participant responses, external data, temporal conditions, and evolving participant state. This design supports everything from simple one-time questionnaires to complex longitudinal studies with sophisticated conditional logic. Built on over a decade of practical experience, CASE underwent major architectural changes in 2024. We replaced a complex microservice design with a streamlined monolithic architecture, significantly improving maintainability and deployment accessibility, particularly for institutions with limited technical resources. CASE has been successfully deployed across diverse domains, powering national disease surveillance platforms, supporting post-COVID cohort studies, and enabling real-time sentiment analysis during political events. These applications, involving tens of thousands of participants, demonstrate the framework's scalability, versatility, and practical value. This paper describes the foundations of CASE, documents its architectural evolution, and shares lessons learned from real-world deployments across diverse research domains and regulatory environments. We position CASE as a mature research infrastructure that balances sophisticated functionality with practical deployment needs for sustainable and institutionally controlled data collection systems.