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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2509.22657 |
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| _version_ | 1866909810125963264 |
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| author | Greiffenstein, Ethan Harris, Trevor Smith, Rebecca |
| author_facet | Greiffenstein, Ethan Harris, Trevor Smith, Rebecca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting West Nile virus with deep graph encoders Greiffenstein, Ethan Harris, Trevor Smith, Rebecca Applications Machine Learning 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. |
| title | Forecasting West Nile virus with deep graph encoders |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2509.22657 |