通过风险图神经网络得出的人口统计学信息热死亡风险曲线
Demographically-Informed Heat-Mortality Risk Curves via Risk Graph Neural Networks
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中文总结 AI 辅助
研究针对环境流行病学中热相关死亡风险估计问题，提出用风险图神经网络（RGNNs）优化分布滞后非线性模型（DLNMs），利用人口普查特征优化系数向量，在英格兰和威尔士多地验证，RGNNs能保持低误差和合理不确定性覆盖。
中文摘要 AI 辅助
估计与热相关的死亡风险是环境流行病学中的核心任务，通常用分布滞后非线性模型（DLNMs）来解决，即拟合温度-死亡时间序列的可解释暴露-反应曲面。DLNMs有效但忽略了人口统计学和地理背景。我们提出风险图神经网络（RGNNs），这是一种分层GNN编码器，利用详细的人口普查特征优化DLNM系数向量，在大幅提高预测校准的同时保留可解释的风险曲线输出。在英格兰和威尔士的10个地区对两个前所未有的炎热年份进行评估，RGNN变体在2022年热浪期间基线崩溃时保持了更低的点误差和接近名义的不确定性覆盖。
英文摘要
Estimating heat-related mortality risk is a core task in environmental epidemiology, typically addressed with Distributed Lag Non-linear Models (DLNMs); interpretable exposure-response surfaces fitted to temperature-mortality time series. DLNMs are effective but ignore demographic and geographic context, despite well-established relevance to heat vulnerability. We propose Risk Graph Neural Networks (RGNNs), a hierarchical GNN encoder that uses granular census features to optimise DLNM coefficient vectors, preserving interpretable risk curve outputs while substantially improving predictive calibration. Evaluated across 10 regions of England and Wales on two unprecedented heat years, RGNN variants maintain both lower point-errors and near-nominal uncertainty coverage during the 2022 heatwave where baselines collapse.
发表机构
- School of Geographical Sciences, University of Bristol（布里斯托大学地理科学学院）
机构由 AI 辅助整理，请以论文原文为准。
