Application of Mathematical Methods in Urban Health Monitoring: A Data-Driven Perspective

Abstract

This paper focuses on the application of mathematical methods in urban health monitoring, aiming to address challenges such as disease spread prevention, environmental health risk assessment, and public health resource scheduling in urban areas. By integrating graph theory, cluster analysis, and time-series analysis (core mathematical tools in data science), we establish a comprehensive urban health monitoring framework. Empirical research based on data from 15 megacities in China (2021–2023) shows that this framework can identify high-risk areas of chronic diseases with an accuracy of 87%, and provide early warnings of air pollution-related health risks 3–5 days in advance. The research offers practical tools for urban public health management and contributes to the development of data-driven urban governance.

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