标题： EpiClim：每周全国各地区多流行病气候健康数据集，用于加速地理健康研究 显示英文标题

标题： EpiClim: Weekly District-Wise all-India multi-epidemics Climate-Health Dataset for accelerated GeoHealth research

摘要： 气候变化对公共健康产生重大影响，推动了流行病的出现和传播。 气候健康模型对于评估和预测受气温和降水等气候变量影响的疾病爆发至关重要。 例如，登革热和疟疾与气温变化相关，而霍乱则与降水量异常有关。 人工智能驱动的天气预测（AI-NWP）技术的进步提高了预测能力，但将气候模型与健康系统结合受到缺乏全面、细致的健康数据集的阻碍。 本研究介绍了EpiClim：印度的流行病-气候数据集，这是印度从2009年至今主要流行病的首个每周县级数据集，数据来源于综合疾病监测计划（IDSP）。 该数据集涵盖登革热、疟疾和急性腹泻病等疾病，弥合了气候与健康数据之间的差距，使气候预测与流行病预测模型的整合成为可能。 这项工作为将预测性气候健康模型与天气和气候模型相结合奠定了基础，推动了应对气候引发的公共卫生危机的努力。 显示英文摘要

摘要： 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.