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Public health emergencies (PHEs) pose significant challenges to global urban governance systems, necessitating the establishment of more efficient and dynamically adaptive response mechanisms. Numerous cases indicate that current urban governance still faces the risk of systemic failure under PHE shocks, leading to severe socio-economic consequences. Existing studies, based on theories such as resilience, emergency management, and risk management, primarily employ traditional statistical modeling or single-discipline approaches to explore improvement pathways. However, they fall short in cross-system and multi-agent coordination mechanisms, as well as data-driven intelligent optimization. Therefore, this project draws inspiration from the principles of the human immune system, introduces the concept of urban immunity to characterize the level of urban governance under PHEs, and follows the approach of “feature decoding → mechanism analysis → spatiotemporal measurement → trend prediction → model optimization → decision output.” It refines the theoretical framework of urban immunity, analyzes urban immune response mechanisms, develops an immunity indicator system, assesses the spatiotemporal patterns of urban immunity, and builds a decision-making model using intelligent optimization methods to generate optimized solutions for different scenarios. Ultimately, the project aims to establish a data-driven, evidence-based decision-making approach. This project seeks to provide a more systematic and operational theoretical framework for urban public health governance while promoting the digital and intelligent transformation of public health management, thereby enhancing PHE prevention and control capabilities.