Enhancing Digital Disease Surveillance in Thailand Using Information Technology, Data Engineering, Data Science, and Artificial Intelligence

Recent advancements in data engineering, data science, and artificial intelligence have revolutionized disease surveillance systems globally. This study examines the implementation of these advancements in Thailand. We integrated these advancements to enhance the four key steps of public health surveillance: data collection, data analysis, data interpretation, and data dissemination. We expanded data collection to include data environment and integration, designing systems to manage multiple sources and facilitating seamless integration. To support analysis and interpretation, we adopted a design thinking approach and developed intuitive tools for exploring disease situations. We identified target users and described the data distribution mechanism. We integrated three major databases: digital disease surveillance, syndromic surveillance, and event-based surveillance, all managed by the Department of Disease Control. Data environments were divided into clusters for extraction, integration, and a data mart for specific use cases. Automated hourly extract-transform-load processes using Apache Airflow facilitated real-time data integration, ensuring seamless data management and timely updates. Data analysis solutions, including automated validation algorithms and business intelligence tools with user-friendly interfaces, were developed according to findings from the design thinking workshop. We developed open data published on dashboards and closed data managed through the Digital Export System. Artificial intelligence-enhanced early warning systems provided notifications of an outbreak to public health authorities via the instant messaging application LINE. In conclusion, the integration of information technology, data engineering, and data science has significantly enhanced Thailand's disease surveillance system, improving data collection, analysis, interpretation, and dissemination of results, leading to more efficient public health responses.