  Analysing and understanding the transmission and evolution of the COVID-19
pandemic is mandatory to be able to design the best social and medical
policies, foresee their outcomes and deal with all the subsequent
socio-economic effects. We address this important problem from a computational
and machine learning perspective. More specifically, we want to statistically
estimate all the relevant parameters for the new coronavirus COVID-19, such as
the reproduction number, fatality rate or length of infectiousness period,
based on Romanian patients, as well as be able to predict future outcomes. This
endeavor is important, since it is well known that these factors vary across
the globe, and might be dependent on many causes, including social, medical,
age and genetic factors. We use a recently published improved version of SEIR,
which is the classic, established model for infectious diseases. We want to
infer all the parameters of the model, which govern the evolution of the
pandemic in Romania, based on the only reliable, true measurement, which is the
number of deaths. Once the model parameters are estimated, we are able to
predict all the other relevant measures, such as the number of exposed and
infectious people. To this end, we propose a self-supervised approach to train
a deep convolutional network to guess the correct set of Modified-SEIR model
parameters, given the observed number of daily fatalities. Then, we refine the
solution with a stochastic coordinate descent approach. We compare our deep
learning optimization scheme with the classic grid search approach and show
great improvement in both computational time and prediction accuracy. We find
an optimistic result in the case fatality rate for Romania which may be around
0.3% and we also demonstrate that our model is able to correctly predict the
number of daily fatalities for up to three weeks in the future.
