  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.
