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In this paper, we propose a parameter identification methodology of the SIRDmodel, an extension of the classical SIR model, that considers the deceased asa separate category. In addition, our model includes one parameter which is theratio between the real total number of infected and the number of infected thatwere documented in the official statistics.  Due to many factors, like governmental decisions, several variantscirculating, opening and closing of schools, the typical assumption that theparameters of the model stay constant for long periods of time is notrealistic. Thus our objective is to create a method which works for shortperiods of time. In this scope, we approach the estimation relying on theprevious 7 days of data and then use the identified parameters to makepredictions.  To perform the estimation of the parameters we propose the average of anensemble of neural networks. Each neural network is constructed based on adatabase built by solving the SIRD for 7 days, with random parameters. In thisway, 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 realdata of Covid19 in Romania and then we illustrate the predictions for differentperiods of time, from 10 up to 45 days, for the number of deaths. The main goalwas to apply this approach on the analysis of COVID-19 evolution in Romania,but this was also exemplified on other countries like Hungary, Czech Republicand Poland with similar results.  The results are backed by a theorem which guarantees that we can recover theparameters of the model from the reported data. We believe this methodology canbe used as a general tool for dealing with short term predictions of infectiousdiseases or in other compartmental models.

Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks