India set to transition from detective to predictive disease surveillance

India is set to make a major leap in strengthening public health security by shifting from a detective-style disease surveillance model to a predictive analysis model, integrating technologies such as artificial intelligence (AI), real-time data analytics and digital intelligence platforms.

The move aims to enhance the country’s ability to detect outbreaks before they escalate. It will help health authorities identify early warning signals prior to clinical manifestation, rapidly mobilise resources, strengthen district-level risk mitigation and prevent large-scale outbreaks through advanced forecasting.

“The aim is to integrate all disease reporting systems into one surveillance system under the umbrella of the Integrated Health Information Platform,” said Dr Ranjan Das, Director, National Centre for Disease Control (NCDC).

Officials at the NCDC said the shift builds on 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), which monitors more than 50 diseases.

“AI picks up early warning signals — even a cluster of cases — and action is taken based on that,” an official said.

One example of NCDC’s AI-powered tools is the Media Scanning and Verification Cell (MSVC), which scans millions of online news reports daily across 13 Indian languages. The tool filters reports related to diseases, their type and location. This information is shared with affected districts, after which the District Surveillance Officer investigates and initiates action.

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

Authorities are still debating whether to expand surveillance to social media platforms. “We are discussing the use of social media for early warning disease control. However, we already have community reporting on the IHIP, where outbreaks can be reported by citizens. To filter out bots, an OTP is required. Once verified, an alert goes to the district concerned and action is taken,” the official added.

He said the shift to predictive surveillance would leverage these analytical capabilities to forecast disease trends and enable intervention even before the first case is reported.

“Typhoid, dengue, malaria and hepatitis A are among the common diseases reported by health facilities on the IHIP. Over 45,000 facilities, including primary health centres and government hospitals, report daily data. We monitor trends continuously and can identify states or districts where cases are rising,” the official said.

Further supporting the transition to predictive surveillance are the newly established Metropolitan Surveillance Units (MSUs) under the PM-Ayushman Bharat Health Infrastructure Mission (PM-ABHIM).

“Metropolitan cities face challenges such as sanitation and waste disposal. These units provide field response for outbreaks. For instance, during suspected paediatric Acute Encephalitis Syndrome cases in Chhindwara district, Madhya Pradesh, the MSU in Nagpur quickly flagged the occurrence to the Central Surveillance Unit, enabling coordination across two states,” another official said.