  We present three data driven model-types for COVID-19 with a minimal number
of parameters to provide insights into the spread of the disease that may be
used for developing policy responses. The first is exponential growth, widely
studied in analysis of early-time data. The second is a self-exciting branching
process model which includes a delay in transmission and recovery. It allows
for meaningful fit to early time stochastic data. The third is the well-known
Susceptible-Infected-Resistant (SIR) model and its cousin, SEIR, with an
"Exposed" component. All three models are related quantitatively, and the SIR
model is used to illustrate the potential effects of short-term distancing
measures in the United States.
