  A finite mixture model is used to learn trends from the currently available
data on coronavirus (COVID-19). Data on the number of confirmed COVID-19
related cases and deaths for European countries and the United States (US) are
explored. A semi-supervised clustering approach with positive equivalence
constraints is used to incorporate country and state information into the
model. The analysis of trends in the rates of cases and deaths is carried out
jointly using a mixture of multivariate Gaussian non-linear regression models
with a mean trend specified using a generalized logistic function. The optimal
number of clusters is chosen using the Bayesian information criterion. The
resulting clusters provide insight into different mitigation strategies adopted
by US states and European countries. The obtained results help identify the
current relative standing of individual states and show a possible future if
they continue with the chosen mitigation technique
