  Our main goal in this paper is to quantitatively compare the performance of
classical methods to XGBoost and convolutional neural networks in a parameter
estimation problem for epidemic spread. As we use flexible two-layer random
graphs as the underlying network, we can also study how much the structure of
the graphs in the training set and the test set can differ while to get a
reasonably good estimate. In addition, we also examine whether additional
information (such as the average degree of infected vertices) can help
improving the results, compared to the case when we only know the time series
consisting of the number of susceptible and infected individuals. Our
simulation results also show which methods are most accurate in the different
phases of the epidemic.
