• Corpus ID: 220936000

Interpretable Sequence Learning for COVID-19 Forecasting

@article{Arik2020InterpretableSL,
  title={Interpretable Sequence Learning for COVID-19 Forecasting},
  author={Sercan {\"O}. Arik and Chun-Liang Li and Jinsung Yoon and Rajarishi S. Sinha and Arkady Epshteyn and Long T. Le and Vikas Menon and Shashank Singh and Leyou Zhang and Nate Yoder and Martin Nikoltchev and Yash Sonthalia and Hootan Nakhost and Elli Kanal and Tomas Pfister},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.00646}
}
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions… 

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