Development of a Dengue Information System (DIS)
Wetthasinghe Arachchige, Udana Kashyapa Wetthasinghe (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052616452
https://urn.fi/URN:NBN:fi:amk-2025052616452
Tiivistelmä
Dengue fever has become an increasingly critical public health concern in Sri Lanka, requiring proactive digital solutions to support awareness, forecasting, and stakeholder collaboration. A web-based system called the Dengue Information System (DIS) was designed and developed to deliver real-time dengue data visualization, integrate forecasting model outputs, and enable user interaction through feedback and research submission features. The primary objective was to implement an effective digital tool tailored to the Sri Lankan context, supporting health professionals, researchers, and the public in dengue prevention efforts.
The system was implemented using a full-stack MERN (MongoDB, Express.js, React, Node.js) framework for the web platform, and Python Flask was used to expose external forecasting models via REST API endpoints. Integration best practices were applied by using JSON formatting, plug-and-play architecture, and minimal dependencies to support model submission and visualization. A mixed-method evaluation approach was used, including two online surveys and a focus group interview to assess usability, performance, forecasting features, and user experience.
High levels of user satisfaction were reported across all evaluated components. The forecasting model integration was found to be effective and credible, with clear visualization outputs and admin-level validation. Interactive charts, data filters, and responsive design features were considered helpful for both understanding and decision-making. Improvements were made during the development cycle based on user feedback, addressing layout, clarity, and usability concerns without major structural changes.
The system was found to be technically sound, user-oriented, and highly applicable to real-world dengue control needs. Opportunities for future enhancements were identified, including real-time integration with government data, environmental predictors, and AI-driven automation for data updates. The development process followed the Design Science Research methodology, resulting in a practical and extensible system capable of supporting public health efforts in dengue surveillance and prevention.
The system was implemented using a full-stack MERN (MongoDB, Express.js, React, Node.js) framework for the web platform, and Python Flask was used to expose external forecasting models via REST API endpoints. Integration best practices were applied by using JSON formatting, plug-and-play architecture, and minimal dependencies to support model submission and visualization. A mixed-method evaluation approach was used, including two online surveys and a focus group interview to assess usability, performance, forecasting features, and user experience.
High levels of user satisfaction were reported across all evaluated components. The forecasting model integration was found to be effective and credible, with clear visualization outputs and admin-level validation. Interactive charts, data filters, and responsive design features were considered helpful for both understanding and decision-making. Improvements were made during the development cycle based on user feedback, addressing layout, clarity, and usability concerns without major structural changes.
The system was found to be technically sound, user-oriented, and highly applicable to real-world dengue control needs. Opportunities for future enhancements were identified, including real-time integration with government data, environmental predictors, and AI-driven automation for data updates. The development process followed the Design Science Research methodology, resulting in a practical and extensible system capable of supporting public health efforts in dengue surveillance and prevention.