Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data

• URL: http://arxiv.org/abs/2212.11367v1
• Date: Wed, 21 Dec 2022 21:08:45 GMT
• Title: Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
• Authors: Adam Tonks (1), Trevor Harris (2), Bo Li (1), William Brown (3), Rebecca Smith (3) ((1) Department of Statistics, University of Illinois at Urbana-Champaign, (2) Department of Statistics, Texas A&M University, (3) Department of Pathobiology, University of Illinois at Urbana-Champaign)
• Abstract summary: We apply a spatially aware graph neural network model to forecast the presence of West Nile virus in Illinois. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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