A Review of Spatial epidemic models in urban healthcare ecosystems: Intelligent Modeling, Electronics Integration, and Real-World Applications

Abstract

Spatial epidemic models have emerged as critical tools for understanding and controlling disease propagation within complex urban healthcare ecosystems characterized by dense populations, heterogeneous mobility patterns, and interconnected infrastructures. This paper presents a comprehensive review of spatial epidemic modeling approaches, emphasizing intelligent modeling techniques, electronics integration, and real-world deployment in urban environments. The study systematically analyzes recent advances from 2018 to 2025, covering mechanistic models, agent-based simulations, data-driven learning frameworks, and hybrid AI-integrated approaches. Particular attention is given to the integration of Internet of Things (IoT) devices, wearable sensors, and edge computing systems that enable real-time data acquisition and dynamic model adaptation. The findings highlight a paradigm shift from static compartmental models toward adaptive, data-driven, and spatially explicit frameworks enhanced by generative artificial intelligence. Contributions of this review include a structured synthesis of 30 key studies, identification of methodological trends, evaluation of strengths and limitations, and the articulation of open challenges in scalability, privacy, and model generalization. The paper further discusses implications for secure and resilient software engineering pipelines in smart healthcare systems.