PULSE: A Laboratory-First Early-Warning Analytics Framework for Population-Level Health Surveillance

This work presents PULSE (Predictive Utilization of Laboratory Surveillance Engine), a research proposal that introduces a laboratory-first analytical framework for population-level early-warning health surveillance. The proposed framework reframes routinely collected laboratory data as upstream physiological signals that may indicate emerging population-level health stress prior to formal case recognition. Rather than focusing on disease diagnosis or individual clinical decision-making, PULSE operates strictly at the population level, transforming aggregated laboratory biomarkers into interpretable surveillance signals. The approach is designed to complement existing outcome-based public-health surveillance systems by enhancing situational awareness without replacing clinical workflows or generating diagnostic or predictive outputs. Methodologically, the proposal integrates validated statistical surveillance models with constrained anomaly-detection techniques within a transparent, ethically governed analytical architecture. Emphasis is placed on interpretability, reproducibility, and governance alignment, ensuring that generated signals support expert interpretation rather than automated decision-making. This document represents an original academic research proposal developed for scholarship and graduate study purposes. A timestamped version is publicly archived to establish authorship and research provenance.