The initiative led by the Indian Council of Medical Research (ICMR) under the National One Health Mission (NOHM) marks a shift from reactive reporting to predictive monitoring. The project will focus on a wide range of threats, including Nipah virus, Zika, bird flu (H5N1) and Kyasanur Forest Disease (monkey fever), among others.
In 2025, India reported 41 outbreaks of bird flu in 10 states, including Maharashtra and Odisha, mainly affecting poultry, wild birds and mammals such as tigers. Crucially, the virus has claimed two human lives. These growing threats are driven by new pathogens ICMR’s new AI mission to improve integrated surveillance and prevent local outbreaks from becoming global pandemics.
The system will provide early signal detection and real-time decision support to prevent local outbreaks from becoming pandemics. The framework will use sophisticated data analytics, including predictive modelling, automated disease surveillance and rapid response coordination.
To this end, the government is expanding its digital and physical infrastructure to manage high-resolution health data. The Integrated Health Information Platform (IHIP) already provides a unified near-real-time reporting system across all 36 states and Union Territories. In addition, the Ayushman Bharat Digital Mission (ABDM) has created a national digital health ecosystem that integrates various health programs and enables creation of digitized records usable for predictive analytics and rapid response.
The initiative recognizes that emerging and re-emerging infectious diseases of zoonotic origin, together with climate-sensitive health risks, pose significant and evolving challenges to the global public health systems. By leveraging advances in AI and data analytics, the National One Health Mission aims to improve integrated disease surveillance.
“This approach is designed to catch pathogens at their source – whether in humans, animals or the environment – before they can spread widely,” said a senior ICMR scientist familiar with the matter on condition of anonymity.
The system uses One Health’s integrated approach to detect “early signals” by simultaneously monitoring unusual patterns across the human, animal and environmental sectors to prevent public panic.
“ICMR has officially invited Expressions of Interest (EoI) from a wide range of eligible organizations, including academic institutions, professional bodies, universities and non-governmental organizations (NGOs), which will be tasked with developing AI-enabled tools that can identify ‘early signals’ of emerging pathogens in all three critical sectors of the One Health framework,” the scientist added.
Predictive non-reactive systems
According to the document, the scope of work is complex and requires organizations not only to design tools but also to integrate them AI solutions for end users and perform passive evaluations at every stage of development. NOHM will provide the necessary research and development funding to support these efforts and ensure that the technology can be validated and scaled up to meet national needs.
Queries sent to ICMR and health ministry spokespersons on Thursday went unanswered.
While the Integrated Disease Surveillance Program (IDSP), under the National Center for Disease Control (NCDC), uses AI, ICMR’s new initiative focuses on primary detection.
“Unlike the current human-centric reporting system, this mission integrates data across the human, animal and environmental sectors to identify zoonotic spillovers early,” the scientist explained.
“AI strengthens disease surveillance by turning scattered signals into effective early warnings. By combining human, animal and environmental data, it can detect unusual patterns – fever clusters, positive lab results, vector changes or livestock deaths – much earlier than manual systems. Predictive models help estimate where an epidemic may spread, while automated dashboards support faster decisions about testing, containment and deployment of resources,” he said Kunma, Drco. AI initiatives at Agilus Diagnostics.
Dr. Sharma further added that AI also reduces reporting delays, improves consistency and helps prioritize high-risk areas for field investigations. “Used responsibly with strong data quality and privacy protections, AI becomes a force multiplier for public health and helps stop local outbreaks before they become pandemics.”
