标题： 基于高分辨率气候数据的温度和湿度相关死亡率的贝叶斯时空模型 显示英文标题

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

摘要： 在本研究中，我们引入了一种新颖且全面的贝叶斯时空疾病映射模型扩展，该模型明确考虑了气象暴露的性别特异性效应。 利用奥地利（2002年至2019年）的精细尺度每周死亡数据和高分辨率气候数据，我们评估温度、湿度、年龄和性别之间的相互作用如何影响死亡模式。 我们的方法超越了传统建模，通过空间-时间、空间-年龄和年龄-时间维度的结构化交互来捕捉复杂的依赖关系，使我们能够捕捉复杂的人口和环境依赖关系。 分析确定了地区层面的死亡模式，并按周量化了与气候相关的风险，为公共卫生监测提供了新的见解。 显示英文摘要

摘要： 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.