A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912487976206336 |
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| author | Perchtold, Corinna Eisenberg, Julia Otto, Philipp |
| author_facet | Perchtold, Corinna Eisenberg, Julia Otto, Philipp |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data Perchtold, Corinna Eisenberg, Julia Otto, Philipp Applications 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. |
| title | A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data |
| topic | Applications |
| url | https://arxiv.org/abs/2507.12643 |