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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.15254 |
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| _version_ | 1866909852458024960 |
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| author | Feng, Dingya Xue, Dingyuan |
| author_facet | Feng, Dingya Xue, Dingyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatiotemporal Transformers for Predicting Avian Disease Risk from Migration Trajectories Feng, Dingya Xue, Dingyuan Machine Learning 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. |
| title | Spatiotemporal Transformers for Predicting Avian Disease Risk from Migration Trajectories |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.15254 |