Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk

@article{Wang2021MachineLS,
  title={Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk},
  author={Lingxiao Wang and Tian Xu and Till Hannes Stoecker and Horst Stoecker and Yin Jiang and Kai Zhou},
  journal={Mach. Learn. Sci. Technol.},
  year={2021},
  volume={2},
  pages={35031}
}
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiotemporal epidemiological model that combines a spatial Cellular Automaton (CA) with a temporal Susceptible-Undiagnosed-Infected-Removed… 
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