A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data

2025-07-16

AI Summary

This study quantifies the nonlinear and heterogeneous effects of temperature and humidity on mortality, while identifying demographic (sex, age) and geographic disparities in vulnerability. To this end, we develop a Bayesian spatiotemporal disease mapping model integrating high-resolution meteorological data with weekly, district-level mortality records from Austria. The model innovatively incorporates sex-specific exposure–response functions and a structured three-dimensional interaction term—spanning space, time, and age—to capture complex multilevel dependencies. It enables fine-grained, weekly, district-level estimation of climate-attributable mortality risk. Results reveal pronounced spatial heterogeneity and heightened vulnerability among specific subpopulations, notably older women. The findings provide actionable, evidence-based support for climate-sensitive public health surveillance systems and targeted, equity-oriented adaptation policies.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements

Abstract

In this study, we introduce a novel and comprehensive extension of a Bayesian spatio-temporal disease mapping model that explicitly accounts for gender-specific effects of meteorological exposures. Leveraging fine-scale weekly mortality and high-resolution climate data from Austria (2002 to 2019), we assess how interactions between temperature, humidity, age, and gender influence mortality patterns. Our approach goes beyond conventional modelling by capturing complex dependencies through structured interactions across space-time, space-age, and age-time dimensions, allowing us to capture complex demographic and environmental dependencies. The analysis identifies district-level mortality patterns and quantifies climate-related risks on a weekly basis, offering new insights for public health surveillance.

Problem

• Modeling gender-specific mortality effects from temperature and humidity
• Assessing interactions between climate, age, and gender on mortality
• Capturing spatio-temporal and demographic dependencies in mortality patterns

Innovation

• Bayesian spatio-temporal model with gender-specific effects
• High-resolution climate and mortality data integration
• Structured spatio-temporal and demographic interaction modeling