Spectral Detection of Logistical Phase Transitions in Pandemics: A Multi-Node Early Warning Framework

This paper introduces a novel computational method for detecting imminent logistical collapse in healthcare infrastructure during pandemic surges. Using the Unified Field Theory-Formalism (UFT-F), we derive a spectral norm indicator, $||V||_{L^{1}}$, which identifies "manifold folding" events—points where hospital coordination becomes non-invertible and NP-hard. Key Features: Empirical Validation: Backtested against NYC, LA, and Chicago COVID-19 waves with a reproducible 5–15 day lead time before logistical crises. Multi-Node Modeling: Includes a metapopulation mobility simulation showing how logistical stress propagates across urban hubs. Fixed Parameterization: Demonstrates results using identical parameters ($\alpha=13, \beta=100$) across diverse datasets to ensure falsifiability. Operational Protocol: Includes a standardized surveillance protocol for public health professionals to monitor system stability in real-time. LODES files are too large/public — link to https://lehd.ces.census.gov/data/lodes/ instead of uploading them.