  In this paper, we apply statistical methods for functional data to explain
the heterogeneity in the evolution of number of deaths of Covid-19 over
different regions. We treat the cumulative daily number of deaths in a specific
region as a curve (functional data) such that the data comprise of a set of
curves over a cross-section of locations. We start by using clustering methods
for functional data to identify potential heterogeneity in the curves and their
functional derivatives. This first stage is an unconditional descriptive
analysis, as we do not use any covariate to estimate the clusters. The
estimated clusters are analyzed as "levels of alert" to identify cities in a
possible critical situation. In the second and final stage, we propose a
functional quantile regression model of the death curves on a number of scalar
socioeconomic and demographic indicators in order to investigate their
functional effects at different levels of the cumulative number of deaths over
time. The proposed model showed a superior predictive capacity by providing
better curve fit at different levels of the cumulative number of deaths
compared to the functional regression model based on ordinary least squares.
