  The analysis of complex longitudinal data such as COVID-19 deaths is
challenging due to several inherent features: (i) Similarly-shaped profiles
with different decay patterns; (ii) Unexplained variation among repeated
measurements within each country, these repeated measurements may be viewed as
clustered data since they are taken on the same country at roughly the same
time; (iii) Skewness, outliers or skew-heavy-tailed noises are possibly
embodied within response variables. This article formulates a robust nonlinear
mixed-effects model based in the class of scale mixtures of skew-normal
distributions for modeling COVID-19 deaths, which allows the analysts to model
such data in the presence of the above described features simultaneously. An
efficient EM-type algorithm is proposed to carry out maximum likelihood
estimation of model parameters. The bootstrap method is used to determine
inherent characteristics of the nonlinear individual profiles such as
confidence interval of the predicted deaths and fitted curves. The target is to
model COVID-19 deaths curves from some Latin American countries since this
region is the new epicenter of the disease. Moreover, since a mixed-effect
framework borrows information from the population-average effects, in our
analysis we include some countries from Europe and North America that are in a
more advanced stage of their COVID-19 deaths curve.
