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

TL;DR

CASE tackles the need for adaptive, context-aware data collection in participatory health research by introducing an event-driven architecture that dynamically adjusts survey flows in response to participant state, external data, and temporal conditions. The work documents a 2024 architectural shift from a complex microservice stack to a simplified monolithic backend, paired with graphical configuration tools that reduce programming requirements. Through real-world deployments across national surveillance networks, post-COVID cohorts, and live-event sentiment studies, the paper demonstrates CASE’s scalability, domain-agnostic applicability, and practical emphasis on privacy and sustainability. Overall, CASE provides a mature, open-source infrastructure that balances sophisticated functionality with maintainable deployment and institutional data sovereignty, enabling rapid adaptation to emerging health threats and long-term interdisciplinary research.

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.

Figures (5)

• Figure 1: A diagram showing the architecture of the CASE framework's study system. Participant events, timers, context sources (such as sensor data or external inputs), and the participant's state (including response history) are processed by a rule-based engine to trigger actions like survey assignments, message scheduling, or external system calls.
• Figure 2: A diagram showing the architecture of the CASE framework's survey module. Context sources including survey definitions, previous and current responses inform the Survey Engine, which processes expressions, generates dynamic content, and controls survey logic to produce an adaptive, context-aware survey experience via the survey renderer.
• Figure 3: CASE Admin UI main menu, providing access to study configuration, participant management, messaging and editors for surveys and expressions. This interface addresses the need for non-technical teams to configure studies without manual coding.
• Figure 4: Thematic microservice architecture, with standalone modules organized by functional responsibility. Orange M labels components required for study management and configuration, while green P label modules necessary for participant facing functionality.
• Figure 5: Simplified architecture optimized for easier deployment. Compared to Figure \ref{['fig:microservices']}, it reduces interdependencies between components and lowers the complexity of setup. Green P labels mark modules required for participant facing applications while orange M labels represent components needed for management functionality.