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Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data. | LitMetric

AI Article Synopsis

  • Machine learning has been increasingly used to tackle geospatial environmental issues like precipitation forecasting and crop yield prediction.
  • Many existing methods for forecasting mosquito populations and diseases overlook the spatial aspects of the data.
  • Our research introduces a spatially aware graph neural network to predict the presence of West Nile virus in Illinois, demonstrating that this approach outperforms traditional methods such as logistic regression and fully-connected neural networks.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220409PMC
http://dx.doi.org/10.1029/2023GH000784DOI Listing

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