  We show that the COVID-19 pandemic under social distancing exhibits universal
dynamics. The cumulative numbers of both infections and deaths quickly cross
over from exponential growth at early times to a longer period of power law
growth, before eventually slowing. In agreement with a recent statistical
forecasting model by the IHME, we show that this dynamics is well described by
the erf function. Using this functional form, we perform a data collapse across
countries and US states with very different population characteristics and
social distancing policies, confirming the universal behavior of the COVID-19
outbreak. We show that the predictive power of statistical models is limited
until a few days before curves flatten, forecast deaths and infections assuming
current policies continue and compare our predictions to the IHME models. We
present simulations showing this universal dynamics is consistent with disease
transmission on scale-free networks and random networks with non-Markovian
transmission dynamics.
