  We propose to use the mean time between infections (MTBI) metric as obtained
from a recently introduced non-homogeneous Markov stochastic model. Different
types of parameter calibration are performed. We estimate the MTBI using data
from different time windows and from the whole stage history and compare the
results. In order to detect waves and stages in the input data, a preprocessing
filtering technique is applied. The results of applying this indicator to the
COVID-19 reported data of infections from Argentina, Germany and the United
States are shown. We find that the MTBI behaves similarly with respect to the
different data inputs, whereas the model parameters completely change their
behaviour. Evolution over time of the parameters and the MTBI indicator is also
shown. We show evidence to support the claim that the MTBI is a rather good
indicator in order to measure the spreading speed of an epidemic, having
similar values whatever the input data size.
