Integrated Machine Learning and Predictive Telemetry for Municipal Public Health Risk Management and Epidemic Vulnerability Mitigation

Municipal public health infrastructure suffers from systemic vulnerabilities rooted in the structural 7- to 14-day diagnostic reporting lags of traditional passive clinical surveillance systems, compelling municipal health authorities into reactive containment postures during early pathogen transmission phases. To eliminate these diagnostic delays, modern big data analytics and predictive telemetry frameworks are essential to expand municipal information capacity and decision-making agility (Alam & Shabbir, 2021). This study presents a unified spatial-temporal predictive telemetry framework that synthesizes multi-modal Internet of Things (IoT) wastewater sensing, microclimatic environmental streams, and high-density mobile spatial-temporal dynamics into an automated, localized Epidemic Vulnerability Index (EVI) using advanced non-linear machine learning architectures specifically Spatial-Temporal Graph Neural Networks (ST-GNN) and Temporal Fusion Transformers (TFT). Evaluated on a synthetic baseline dataset representative of high-density metropolitan regions, this multi-tier telemetry integration reduces early-outbreak detection delays by 8.4 days compared to clinical benchmarks, while the ST-GNN architecture achieves superior predictive accuracy (RMSE = 0.114, MAE = 0.082) across a 14-day forecasting horizon, outperforming classical compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) models and spatial autoregressive baselines. Sensitivity analyses confirm that non-linear interaction effects between ambient microclimatic anomalies and transit mobility fluxes drive intra-urban transmission velocity aligning with non-linear modeling principles established in predictive systems analytics (Haque & Rasel-Ul-Alam, 2018). By embedding heterogeneous data streams into an integrated operational pipeline, the framework provides health administrators with a robust enterprise risk mitigation architecture (Alam, 2021) to optimize resource allocation, trigger targeted early interventions and minimize municipal epidemic vulnerability.