Integrated Spatio-Temporal Air Quality and Healthcare Burden Forecasting: A Novel Information System for Public Health Management in Transitioning Urban Environments

Abstract

In a rapidly transforming urban environment like Nagpur, which is experiencing accelerated industrial development and environmental changes, accurate air quality forecasting and its correlation with healthcare outcomes have become critically important. This paper introduces an integrated spatio-temporal information system for air quality and healthcare burden forecasting, designed specifically for public health management in transitioning urban environments. Our system combines three state-of-the-art models: HT-CNN-MSSA (Hybrid Transformer-CNN with Multiple Scale Spatial Awareness), GNN-ESTA (Graph Neural Network with Epidemic Spatio-Temporal Attention), and DV-STGAE (Dynamic Variational Spatio-Temporal Graph Autoencoder).

This integrated approach significantly outperforms traditional methods in capturing complex spatio-temporal dependencies and sudden shifts in air quality, improving forecasting accuracy by 15-25% compared to conventional LSTM networks. The system demonstrates a strong correlation (0.85) with real-world healthcare outcomes, particularly in regions with heterogeneous population densities. It provides an 18% improvement in air quality prediction and a 20% increase in disease incidence forecasting accuracy. Moreover, our system captures sudden shifts in air quality with 25% higher accuracy compared to conventional methods, achieving an R-squared value of 0.88 for healthcare burden predictions.

The integrated nature of our system allows for seamless data flow from various sources, including environmental sensors, healthcare facilities, and population mobility data. This comprehensive approach enables public health officials to make data-driven decisions in rapidly changing urban landscapes, facilitating proactive measures to mitigate health risks associated with poor air quality. By bridging the gap between environmental monitoring and public health forecasting, our system represents a significant advancement in managing the complex interplay between urbanization, air quality, and public health in transitioning environments.