In a significant push to modernise public health infrastructure using technology and predictive analytics, Health Secretary G Veerapandian announced on Wednesday that the Andhra Pradesh government is setting up a specialised Disease Monitoring and Decision Support Centre (DMDC) to anticipate public health risks, track disease spread, and strengthen preventive healthcare services across the State.
Addressing a state-level workshop held in Vijayawada on the operationalisation of the DMDC—established in collaboration with the Bill & Melinda Gates Foundation, National University of Singapore, and IIT Bombay—Veerapandian emphasised that data-driven decision-making must become the core of public health planning and execution.
The Secretary highlighted the ‘AWARE’ disease model, which integrates health data to forecast communicable and non-communicable disease patterns. High-resolution mapping enables tracking of disease severity and outbreaks up to mandal, village and Grama/Ward Sachivalayam levels. Maternal and infant mortality maps help identify high-risk areas and target interventions. Veerapandian directed medical officers, ANMs and ASHA workers to use analytics for outbreak prevention and ordered plans and alternative networks for remote, tribal and border areas.
During the workshop, experts and around 80 state-level nodal officers discussed integrating Artificial Intelligence (AI), Machine Learning (ML), and advanced epidemic modelling into the DMDC framework to transition from treatment to predictive prevention.
In its first year, the centre will focus on 3 to 5 high-priority, actionable goals per health initiative, covering infectious diseases, non-communicable conditions, and maternal-child health.
NTR University of Health Sciences has been designated as the primary academic and coordinating institution for the centre, while operational command and policy decisions will remain strictly under the State government. Experts noted that the ultimate benchmark for the DMDC’s success would not merely be generating data models, but driving timely field-level action and improving clinical outcomes.
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