"Getting the data into a model" is essentially the process of fitting the adjustable parameters of a model so that the model predicts the data as closely as possible. One needs to fit the data for each country separately. For the influenza models I was able to use NonlinearModelFit because there were data available for only one compartment. For the COVID-19 models, I'm trying to fit confirmed case, recovered cases, and deaths, so I've been using NMinimize along with a custom function that computes the sum of squared errors of the model with a given set of parameter values against the observed data. So far, I have not found a model structure that allows me to do this with a sufficient degree of accuracy. Success depends very much on the choice of terms in the model, which is equivalent to the compartments and connections in the compartmental models I'm using.