  In this paper, we propose a parameter identification methodology of the SIRD
model, an extension of the classical SIR model, that considers the deceased as
a separate category. In addition, our model includes one parameter which is the
ratio between the real total number of infected and the number of infected that
were documented in the official statistics.
  Due to many factors, like governmental decisions, several variants
circulating, opening and closing of schools, the typical assumption that the
parameters of the model stay constant for long periods of time is not
realistic. Thus our objective is to create a method which works for short
periods of time. In this scope, we approach the estimation relying on the
previous 7 days of data and then use the identified parameters to make
predictions.
  To perform the estimation of the parameters we propose the average of an
ensemble of neural networks. Each neural network is constructed based on a
database built by solving the SIRD for 7 days, with random parameters. In this
way, the networks learn the parameters from the solution of the SIRD model.
  Lastly we use the ensemble to get estimates of the parameters from the real
data of Covid19 in Romania and then we illustrate the predictions for different
periods of time, from 10 up to 45 days, for the number of deaths. The main goal
was to apply this approach on the analysis of COVID-19 evolution in Romania,
but this was also exemplified on other countries like Hungary, Czech Republic
and Poland with similar results.
  The results are backed by a theorem which guarantees that we can recover the
parameters of the model from the reported data. We believe this methodology can
be used as a general tool for dealing with short term predictions of infectious
diseases or in other compartmental models.

Machine Learning;Populations and Evolution;Quantitative Methods