  Accurate forecasts of COVID-19 is central to resource management and building
strategies to deal with the epidemic. We propose a heterogeneous infection rate
model with human mobility for epidemic modeling, a preliminary version of which
we have successfully used during DARPA Grand Challenge 2014. By linearizing the
model and using weighted least squares, our model is able to quickly adapt to
changing trends and provide extremely accurate predictions of confirmed cases
at the level of countries and states of the United States. We show that during
the earlier part of the epidemic, using travel data increases the predictions.
Training the model to forecast also enables learning characteristics of the
epidemic. In particular, we show that changes in model parameters over time can
help us quantify how well a state or a country has responded to the epidemic.
The variations in parameters also allow us to forecast different scenarios such
as what would happen if we were to disregard social distancing suggestions.
