无需历史死亡数据，用深度学习预测致命热浪
Modular Deep-Learning-Based Early Warning System for Deadly Heatwave Prediction
- 分两阶段预测基础死亡率与热浪相关死亡增量
- 在西班牙多区域验证，准确率高且鲁棒性强
- 可调误报与漏报平衡，适合城市应急决策
城市严重热浪对公共健康构成重大威胁，亟需建立早期预警机制。尽管已有研究能预测热浪发生并归因历史死亡，但预测即将来临的致命热浪仍具挑战性，主要源于难以定义和估算热相关死亡。此外，构建预警系统还需考虑数据可用性、时空鲁棒性及决策成本。为此，我们提出DeepTherm——一种无需热相关死亡历史数据的模块化早期预警系统。通过深度学习的灵活性，DeepTherm采用双阶段预测流程，将无热浪情况下的基础死亡率及其他异常事件导致的死亡从总死亡率中分离。我们在西班牙真实数据上评估了DeepTherm，结果表明其在不同地区、时间周期和人群群体中均表现一致、稳健且准确，同时支持误报与漏报之间的权衡调整。
原文摘要 · Abstract (English)
Severe heatwaves in urban areas significantly threaten public health, calling for establishing early warning strategies. Despite predicting occurrence of heatwaves and attributing historical mortality, predicting an incoming deadly heatwave remains a challenge due to the difficulty in defining and estimating heat-related mortality. Furthermore, establishing an early warning system imposes additional requirements, including data availability, spatial and temporal robustness, and decision costs. To address these challenges, we propose DeepTherm, a modular early warning system for deadly heatwave prediction without requiring heat-related mortality history. By highlighting the flexibility of deep learning, DeepTherm employs a dual-prediction pipeline, disentangling baseline mortality in the absence of heatwaves and other irregular events from all-cause mortality. We evaluated DeepTherm on real-world data across Spain. Results demonstrate consistent, robust, and accurate performance across diverse regions, time periods, and population groups while allowing trade-off between missed alarms and false alarms.
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