India moves to predictive disease surveillance using AI

India is set to transform its disease surveillance approach from traditional detective methods to a predictive model by integrating cutting-edge technologies such as Artificial Intelligence (AI), real-time data analytics, and digital intelligence platforms.

This paradigm shift will significantly enhance the country’s ability to identify outbreaks before they escalate, enabling faster decision-making, rapid response, and proactive containment.

Officials at the National Centre for Disease Control (NCDC) said the move builds upon the success of AI-based event surveillance systems already in use under the Integrated Health Information Platform (IHIP) of the Integrated Disease Surveillance Programme (IDSP).

The Media Scanning and Verification Cell (MSVC) has been leveraging an AI-powered pipeline that scans millions of online news reports daily across 13 Indian languages, extracting structured health event data, including the disease type, location, and scale.

The system has already processed over 300 million news articles since 2022, flagging more than 95,000 unique health-related events - a 150 per cent increase in detection capacity over manual systems, with 98 per cent reduction in workload for surveillance teams, they said.

This transformative technology, known as Health Sentinel, acts as a “digital watchdog”, automatically identifying unusual spikes in diseases like dengue, chikungunya and other public health threats, which are then verified by experts for accuracy.

The shift to predictive surveillance will leverage these powerful analytical capabilities to forecast disease trends and enable intervention even before the first case is reported, marking a major stride in India’s pandemic preparedness, an official said.

Further strengthening this transition, the newly established Metropolitan Surveillance Units (MSUs) under the PM-Ayushman Bharat Health Infrastructure Mission (PM-ABHIM) have demonstrated real-time surveillance capabilities, officials said.

In a recent incident involving suspected paediatric Acute Encephalitis Syndrome (AES) cases in Chhindwara district, Madhya Pradesh, the MSU Nagpur promptly flagged the occurrence to the Central Surveillance Unit, enabling rapid coordination among stakeholders across two States.

Swift expert deployment by the National Joint Outbreak Response Team (NJORT), in collaboration with ICMR, NIE and CDSCO, helped mount an immediate field response.

The case illustrates the evolving capacity of India’s surveillance ecosystem to rapidly detect unusual clinical patterns and trigger early intervention even in complex urban health settings, officials said.

This approach also underscores the focus on collaborative surveillance that IDSP, NCDC has embarked upon and is cementing it further.

Experts note that the upcoming predictive model will integrate AI surveillance, laboratory intelligence, climatic data, population movement patterns, and digital diagnostics to anticipate outbreak trajectories.

Since 2022, the system flagged over 95,000 health events from more than 300 million articles, cutting manual workload by 98 per cent