AI-Driven Genomic Surveillance Systems for Public Health Governance

An IoT-Integrated Framework for Smart Cities

Abstract

Rapid urbanization, climate-driven ecological changes, increased human mobility, and the accelerated evolution of infectious agents underscore the urgent need for next-generation, real-time public health intelligence systems in smart-city environments. Conventional surveillance approaches remain largely reactive, fragmented, and dependent on delayed laboratory reporting, limiting their effectiveness in densely populated urban settings. This paper proposes an AI-driven genomic surveillance system (AIGSS) that integrates IoT-enabled biosensing, distributed genomic sequencing, and predictive artificial intelligence models to support proactive public health monitoring and governance. Using the Issue Identification, Review, Methodology, Analysis, and Discussion (IRMAD) framework as a conceptual system design methodology, we develop a multi-layered surveillance architecture encompassing data acquisition, edge preprocess, AI-based genomic analytics, and governance-oriented decision support. The proposed framework is evaluated through simulation-based analysis and benchmarking against performance trends reported in existing surveillance systems. Results indicate that AI-enabled integration of genomic and IoT data has the potential to substantially reduce outbreak detection latency and improve variant classification performance when compared to traditional approaches. Rather than presenting real-world clinical validation, this study focuses on architectural feasibility, analytical workflow design, and governance implications. Beyond technical performance, the framework emphasizes ethical data governance, transparency, accountability, and equitable access, aligning genomic intelligence with smart-city public health objectives. This work contributes a scalable and policy-aware reference model for the convergence of AI, genomics, and IoT, providing a foundation for future empirical validation and deployment in urban public health surveillance systems.