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.

翻译：本文提出CASE，一个用于自适应参与式研究与疾病监测的开源框架。与传统采用静态分支逻辑的调查平台不同，CASE采用事件驱动架构，能够根据参与者响应、外部数据、时间条件及动态变化的参与者状态实时调整调查流程。该设计支持从简单的一次性问卷到具有复杂条件逻辑的纵向研究等多种应用场景。基于十余年的实践经验，CASE在2024年进行了重大架构革新：我们将复杂的微服务设计替换为精简的单体架构，显著提升了系统的可维护性与部署便捷性，尤其适用于技术资源有限的机构。CASE已在多个领域成功部署，包括国家级疾病监测平台、后疫情时代队列研究支持，以及政治事件期间的实时舆情分析。这些涉及数万参与者的应用案例，充分证明了该框架的可扩展性、多功能性与实用价值。本文阐述了CASE的设计原理，记录了其架构演进历程，并分享了在不同研究领域与监管环境下实际部署的经验教训。我们将CASE定位为成熟的研究基础设施，它在复杂功能与实用部署需求之间取得平衡，致力于构建可持续且受机构管控的数据收集系统。