<![CDATA[Rapid urbanization, increasing population density, extensive human mobility, and interconnected healthcare systems have intensified the complexity of infectious disease transmission, creating challenges for timely outbreak detection and effective public health response. Conventional epidemiological approaches often struggle to represent dynamic transmission patterns and changing urban conditions, limiting intervention planning. This study evaluated the effectiveness of mathematical modeling and statistical analysis in predicting disease outbreaks and optimizing public health interventions in urban environments. A mixed-methods sequential explanatory design was employed using approximately 2.4 million anonymized surveillance records collected from 185 hospitals, 420 primary healthcare centers, and eight metropolitan surveillance systems over ten years. Quantitative analyses integrated compartmental epidemic models, Bayesian inference, spatial epidemiological analysis, time-series forecasting, multivariate statistics, structural equation modeling, hierarchical regression, mediation, and moderation analyses, while qualitative evidence was analyzed through thematic analysis. Findings showed that integrated mathematical and statistical models significantly improved outbreak prediction accuracy, intervention timing, healthcare preparedness, resource allocation, and response efficiency. Prediction accuracy enhanced intervention effectiveness, whereas surveillance integration and institutional coordination strengthened healthcare resilience, supporting evidence-based decision-making and adaptive public health governance during infectious disease outbreaks.]]>
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