AI-DRIVEN mRNA VACCINE STRATEGIES FOR EMERGING ZOONOTIC DISEASES IN URBAN INDIA: A TRANSLATIONAL PUBLIC HEALTH FRAMEWORK

Abstract

BACKGROUND AND OBJECTIVE:

Emerging zoonotic diseases pose increasing threats to urban populations in India, exacerbated by high population density, rapid urbanization, and global connectivity. mRNA vaccines, accelerated through artificial intelligence (AI)-guided design, offer a promising translational public health intervention to prevent outbreaks. This study evaluates the framework for deploying AI-driven mRNA vaccine strategies for emerging zoonotic diseases in urban India.

METHODS:

A translational public health framework was developed integrating epidemiological modeling, AI-based antigen prediction, and vaccine distribution planning. Urban population data, zoonotic disease incidence reports (2015–2025), and health infrastructure metrics were analyzed. The framework incorporates predictive modeling for outbreak hotspots, real-time vaccine efficacy simulation, and scenario-based deployment strategies.

RESULTS:

AI-guided antigen prediction reduced mRNA candidate selection time by 65% compared to traditional methods. Urban hotspot mapping identified 12 high-risk districts in five metropolitan cities. The framework projected a 48% reduction in outbreak incidence with rapid mRNA vaccine deployment coupled with targeted community vaccination. Cost-effectiveness analysis indicated a 1.8-fold improvement in resource utilization for vaccine prodsuction and distribution.

CONCLUSION AND IMPLICATIONS FOR TRANSLATION:

AI-driven mRNA vaccine strategies offer a feasible and scalable approach for controlling emerging zoonotic diseases in Indian urban contexts. Integrating AI with translational public health planning can optimize vaccine design, deployment efficiency, and outbreak mitigation, informing policy-level decisions for epidemic preparedness.