• Corpus ID: 230437607

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

@article{Fritz2021CombiningGN,
  title={Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany},
  author={Cornelius Fritz and Emilio Dorigatti and D. R{\"u}gamer},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.00661}
}
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. Several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches are often applying standard models of the respective research field. This habit, however, often comes along with… 

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