Digital Disease Surveillance

The initiative tackles the challenges of inefficient and fragmented disease surveillance systems, which limit timely detection, analysis, and response to public health threats. By integrating data from diverse sources and automating real-time data management, it overcomes issues of delayed reporting, incomplete data, and lack of interoperability between systems. Additionally, it addresses the need for intuitive tools to analyze and interpret disease trends, ensuring actionable insights for public health authorities. The initiative also focuses on enhancing data dissemination to target stakeholders, improving communication and facilitating prompt responses to outbreaks.

Initiative Name: Advancing Disease Surveillance through Data Integration and AI in Thailand

This initiative leverages advancements in data engineering, data science, and artificial intelligence to revolutionize disease surveillance systems in Thailand. By enhancing the four critical steps of public health surveillance—data collection, analysis, interpretation, and dissemination—the initiative aims to create a more responsive and efficient public health infrastructure.

Key improvements include the expansion of data collection to encompass diverse sources and integration environments, enabling seamless management through systems designed for multi-source interoperability. For data analysis and interpretation, a design thinking approach guided the development of intuitive tools that provide insights into disease trends and situations. Dissemination efforts focused on identifying target users and crafting effective data distribution mechanisms, ensuring information reaches the right stakeholders.

The initiative integrates three major databases: digital disease surveillance (connecting data from 5,432 facilities via APIs), syndromic surveillance, and event-based surveillance, all managed by the Department of Disease Control. Data environments were structured into clusters for extraction, integration, and tailored data marts for specific use cases. Real-time data management was achieved through automated hourly extract-transform-load (ETL) processes powered by Apache Airflow, ensuring timely and accurate updates.

Innovative data analysis solutions include automated validation algorithms and business intelligence tools with user-friendly interfaces, developed based on feedback from design thinking workshops. Open data dashboards and closed data systems managed through the Digital Export System further facilitate data accessibility and control. Additionally, an AI-enhanced early warning system notifies public health authorities of potential outbreaks via the LINE application, enabling prompt responses.

In summary, this initiative demonstrates the transformative impact of integrating IT, data engineering, and AI into Thailand’s disease surveillance systems, resulting in improved efficiency across data collection, analysis, interpretation, and dissemination, and contributing to more effective public health responses.

The proposed solution adds value to other available solutions by integrating advancements in data engineering, data science, and artificial intelligence to address the limitations of traditional disease surveillance systems. Unlike fragmented systems, this solution connects multiple data sources—including digital disease surveillance, syndromic surveillance, and event-based surveillance—through seamless API integration and real-time ETL processes powered by Apache Airflow.

The solution improves efficiency and accuracy in data collection, management, and dissemination by enabling automated validation, real-time updates, and tailored data marts for specific use cases. It enhances data analysis and interpretation through intuitive tools developed using a design thinking approach, ensuring insights are accessible to public health stakeholders. Additionally, the AI-enhanced early warning system notifies health authorities of potential outbreaks via the LINE application, enabling timely responses and proactive measures.

Importantly, this solution serves as a data source for other available solutions, such as Rootan, which communicates surveillance data to the general public, thereby improving public awareness and engagement with real-time health information.

In comparison to existing solutions, this initiative streamlines data interoperability, promotes actionable insights, and improves overall responsiveness, resulting in a robust, scalable, and efficient disease surveillance system that meets both national and international public health demands.