用新型图神经网络提升西尼罗河病毒预测精度，支持大规模数据训练。
Forecasting West Nile virus with deep graph encoders
- 设计线性连接的图注意力层，聚焦局部信息避免过平滑。
- 在伊利诺伊州多源数据上训练，预测性能显著优于传统方法。
- 适合公共卫生部门做病毒爆发预警，尤其适用于长期预测。
西尼罗河病毒是美国日益严重的公共健康问题，目前尚无人类疫苗，蚊虫控制依赖精准预测来决定防控时机与区域。近期空间图神经网络（GNN）在该领域表现优异，超越传统方法。本文提出一种新型GNN变体，通过线性连接图注意力层，实现更大规模模型的训练，同时优化密集连接结构，使模型更关注局部信息，缓解过平滑问题。为支持大规模训练，我们构建了一个涵盖伊利诺伊州气象数据、土地利用信息和蚊虫监测结果的大型新数据集。实验表明，该方法在多种场景下，对不同预测时长的西尼罗河病毒爆发预测，均显著优于现有GNN及经典基线模型，无论是在样本外还是跨图预测中表现更优。
原文摘要 · Abstract (English)
West Nile virus is a significant, and growing, public health issue in the United States. With no human vaccine, mosquito control programs rely on accurate forecasting to determine when and where WNV will emerge. Recently, spatial Graph neural networks (GNNs) were shown to be a powerful tool for WNV forecasting, significantly improving over traditional methods. Building on this work, we introduce a new GNN variant that linearly connects graph attention layers, allowing us to train much larger models than previously used for WNV forecasting. This architecture specializes general densely connected GNNs so that the model focuses more heavily on local information to prevent over smoothing. To support training large GNNs we compiled a massive new dataset of weather data, land use information, and mosquito trap results across Illinois. Experiments show that our approach significantly outperforms both GNN and classical baselines in both out-of-sample and out-of-graph WNV prediction skill across a variety of scenarios and over all prediction horizons.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。