Govt Taps Private Sector, Academia To Build India's AI Pandemic Warning System

(MENAFN- Live Mint)

New Delhi: Facing a surge in zoonotic threats, India is set to use artificial intelligence (AI) to detect emerging pathogens that jump from animals to humans, according to officials and documents reviewed by Mint.

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 surveillance. The project will target a broad spectrum of threats, including Nipah virus, Zika, Avian Influenza (H5N1), and Kyasanur Forest Disease (monkey fever), among others.

In 2025, India reported 41 bird flu outbreaks across 10 states, including Maharashtra and Odisha, primarily affecting poultry, wild birds, and mammals like tigers. Crucially, the virus caused two human fatalities. These rising novel pathogen threats drive the ICMR 's new AI mission to enhance 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 escalating into pandemics. The framework will utilize sophisticated data analytics, including predictive modelling, automated disease surveillance, and rapid response coordination.

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Towards 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. Furthermore, the Ayushman Bharat Digital Mission (ABDM) has created a national digital health ecosystem that integrates various health programmes, enabling the creation of digitized records usable for predictive analytics and rapid response.

The initiative acknowledges that emerging and re-emerging infectious diseases of zoonotic origin, alongside climate-sensitive health risks, pose significant and evolving challenges to global public health systems. By leveraging advances in AI and data analytics, the National One Health Mission aims to enhance 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," a senior ICMR scientist familiar with the matter said on condition of anonymity.

The system employs an integrated One Health approach to detect 'early signals' by simultaneously monitoring unusual patterns across human, animal, and environmental sectors to avoid public panic.

“The ICMR has officially invited Expressions of Interest (EoI) from a wide range of eligible organizations, including academic institutions, professional bodies, universities, and NGOs (non-governmental organizations) who will be tasked with developing AI-enabled tools that can identify 'early signals' of novel pathogens across all three critical sectors of the One Health framework," the scientist added.

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Predictive not reactive systems

According to the document, the scope of work is comprehensive, requiring organizations to not only design the tools but also integrate these AI solutions for end-users and undertake passive evaluations at every stage of development. The NOHM will provide the necessary R&D funding to support these efforts, ensuring that the technology can be validated and scaled up to meet national needs.

Queries sent to ICMR and health ministry spokespersons on Thursday remained unanswered.

While the Integrated Disease Surveillance Programme (IDSP), under the National Centre for Disease Control (NCDC), is using AI, the ICMR's new initiative focuses on primary detection.

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“Unlike the current human-centric reporting system, this mission integrates data across human, animal, and environmental sectors to identify zoonotic spillovers early," the scientist explained.

“AI strengthens disease surveillance by turning scattered signals into actionable early warnings. By combining human, animal, and environmental data, it can spot unusual patterns-fever clusters, lab positives, vector changes, or livestock deaths-far earlier than manual systems. Predictive models help estimate where an outbreak may spread next, while automated dashboards support faster decisions on testing, containment, and resource deployment," said Dr. Kunal Sharma, VP, Integrated Oncopathology and AI initiatives at Agilus Diagnostics.

Dr. Sharma further added AI also reduces reporting delays, improves consistency, and helps prioritize high-risk areas for field investigation.“Used responsibly with strong data quality and privacy safeguards, AI becomes a force multiplier for public health, helping stop local outbreaks before they become pandemics."

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