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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2501.18602 |
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| _version_ | 1866909594685538304 |
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| author | Kaur, Gurleen Ghoshal, Shubham Marbate, Reena Malviya, Neetiraj Kaur, Arshmehar SB, Vaisakh Srivastava, Amit Kumar Singh, Manmeet |
| author_facet | Kaur, Gurleen Ghoshal, Shubham Marbate, Reena Malviya, Neetiraj Kaur, Arshmehar SB, Vaisakh Srivastava, Amit Kumar Singh, Manmeet |
| contents | Climate change significantly impacts public health, driving the emergence and spread of epidemics. Climate health models are essential for assessing and predicting disease outbreaks influenced by climatic variables like temperature and precipitation. For instance, dengue and malaria correlate with temperature changes, while cholera is linked to precipitation anomalies. Advances in AI-enabled weather prediction (AI-NWP) have improved forecasting, but integrating climate models with health systems is hindered by the lack of comprehensive, granular health datasets. This study introduces EpiClim: India's Epidemic-Climate Dataset, the first weekly district-wise dataset for major epidemics in India from 2009 to the present, sourced from the Integrated Disease Surveillance Programme (IDSP). The dataset, covering diseases like dengue, malaria, and acute-diarrheal disease, bridges the gap between climate and health data, enabling the integration of climate forecasts with epidemic prediction models. This work lays the foundation for coupling predictive climate health models with weather and climate models, advancing efforts to mitigate climate-induced public health crises. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EpiClim: Weekly District-Wise all-India multi-epidemics Climate-Health Dataset for accelerated GeoHealth research Kaur, Gurleen Ghoshal, Shubham Marbate, Reena Malviya, Neetiraj Kaur, Arshmehar SB, Vaisakh Srivastava, Amit Kumar Singh, Manmeet Physics and Society Climate change significantly impacts public health, driving the emergence and spread of epidemics. Climate health models are essential for assessing and predicting disease outbreaks influenced by climatic variables like temperature and precipitation. For instance, dengue and malaria correlate with temperature changes, while cholera is linked to precipitation anomalies. Advances in AI-enabled weather prediction (AI-NWP) have improved forecasting, but integrating climate models with health systems is hindered by the lack of comprehensive, granular health datasets. This study introduces EpiClim: India's Epidemic-Climate Dataset, the first weekly district-wise dataset for major epidemics in India from 2009 to the present, sourced from the Integrated Disease Surveillance Programme (IDSP). The dataset, covering diseases like dengue, malaria, and acute-diarrheal disease, bridges the gap between climate and health data, enabling the integration of climate forecasts with epidemic prediction models. This work lays the foundation for coupling predictive climate health models with weather and climate models, advancing efforts to mitigate climate-induced public health crises. |
| title | EpiClim: Weekly District-Wise all-India multi-epidemics Climate-Health Dataset for accelerated GeoHealth research |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2501.18602 |