用迁徙轨迹预测鸟类疫病风险，准确率达98.2%
Spatiotemporal Transformers for Predicting Avian Disease Risk from Migration Trajectories
- 基于Transformer建模迁徙路径的时空依赖关系
- 在测试集上达0.9821准确率，AUC达0.9803
- 适合野生动物保护与公共卫生预警系统
准确预测鸟类疾病爆发对野生动植物保护和公共健康至关重要。本研究提出一种基于Transformer的框架，用于预测候鸟迁徙路径终点处的疾病风险。融合多源数据：Movebank的GPS追踪数据、世界动物卫生组织（WOAH）的疫情记录，以及GADM和Natural Earth的地理空间信息。原始坐标通过H3分层地理编码处理以捕捉空间模式。模型从鸟类移动序列中学习时空依赖性，估算终点疾病风险。在留出测试集上的评估显示，预测性能优异，准确率为0.9821，受试者工作特征曲线下面积（AUC）为0.9803，平均精度（AP）为0.9299，F1分数在最优阈值下达0.8836。结果表明，Transformer架构可有效支持禽类疾病早期预警系统，助力及时干预与防控策略制定。
原文摘要 · Abstract (English)
Accurate forecasting of avian disease outbreaks is critical for wildlife conservation and public health. This study presents a Transformer-based framework for predicting the disease risk at the terminal locations of migratory bird trajectories. We integrate multi-source datasets, including GPS tracking data from Movebank, outbreak records from the World Organisation for Animal Health (WOAH), and geospatial context from GADM and Natural Earth. The raw coordinates are processed using H3 hierarchical geospatial encoding to capture spatial patterns. The model learns spatiotemporal dependencies from bird movement sequences to estimate endpoint disease risk. Evaluation on a held-out test set demonstrates strong predictive performance, achieving an accuracy of 0.9821, area under the ROC curve (AUC) of 0.9803, average precision (AP) of 0.9299, and an F1-score of 0.8836 at the optimal threshold. These results highlight the potential of Transformer architectures to support early-warning systems for avian disease surveillance, enabling timely intervention and prevention strategies.
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