Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866913419668488192 |
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| author | Tonks, Adam Harris, Trevor Li, Bo Brown, William Smith, Rebecca |
| author_facet | Tonks, Adam Harris, Trevor Li, Bo Brown, William Smith, Rebecca |
| contents | Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_11367 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data Tonks, Adam Harris, Trevor Li, Bo Brown, William Smith, Rebecca Populations and Evolution Machine Learning Quantitative Methods Applications Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks. |
| title | Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data |
| topic | Populations and Evolution Machine Learning Quantitative Methods Applications |
| url | https://arxiv.org/abs/2212.11367 |