Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics

@article{MagdonIsmail2020MachineLT,
  title={Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics},
  author={Malik Magdon-Ismail},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.07602}
}
We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal… 

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