  It is well known that the confirmed COVID-19 infection is only a fraction of
the true fraction. In this paper we use an artificial neural network to learn
the connection between the confirmed infection count, the testing data, and the
true infection count. The true infection count in the training set is obtained
by backcasting from the death count and the infection fatality ratio (IFR).
Multiple factors are taken into consideration in the estimation of IFR. We also
calibrate the recovered true COVID-19 case count with an SEIR model.
