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

We present a data-driven machine learning analysis of COVID-19 from its \emph{early} infection dynamics, with the goal of extracting actionable public health insights. We focus on the transmission dynamics in the USA starting from the first confirmed infection on January 21 2020. We find that COVID-19 has a strong infectious force if left unchecked, with a doubling time of under 3 days. However it is not particularly virulent. Our methods may be of general interest.