National Centre for Disease Control's Transition to Predictive Disease Surveillance
The National Centre for Disease Control (NCDC) is transitioning from traditional disease detection methods to a predictive surveillance model in India. This transition is powered by artificial intelligence (AI), real-time data analytics, and digital intelligence platforms, aiming to bolster public health security.
Development of Predictive Model
- The upcoming predictive model will integrate multiple data sources:
- AI surveillance
- Laboratory intelligence
- Climatic data
- Population movement patterns
- Digital diagnostics
This model will anticipate outbreak trajectories, enhancing India's ability to manage public health threats.
Existing Systems and Achievements
- The model expands upon the existing AI-based event surveillance systems under the Integrated Health Information Platform (IHIP) of the Integrated Disease Surveillance Programme (IDSP).
- The Media Scanning and Verification Cell (MSVC) under IDSP uses AI technology to:
- Scan millions of online news reports daily in 13 Indian languages.
- Extract structured health event data, including disease type, location, and scale.
- Since 2022, the system has processed over 300 million news articles and flagged more than 95,000 unique health-related events.
Impact and Future Goals
- The predictive surveillance approach aims to:
- Forecast disease trends.
- Enable interventions before the first case is reported.
- Empower health authorities to detect early warning signals.
- Mobilize resources and field teams rapidly.
- Strengthen district-level risk mitigation.
- Metropolitan Surveillance Units (MSUs) under the Pradhan Mantri Ayushman Bharat Health Infrastructure Mission (PM-ABHIM) have shown real-time surveillance capabilities, further supporting this transition.
This strategic shift represents a significant advancement in India's pandemic preparedness and public health response.