  We consider smooth nonparametric estimation of the incubation time
distribution of COVID-19, in connection with the investigation of researchers
from the National Institute for Public Health and the Environment (Dutch: RIVM)
of 88 travelers from Wuhan: Backer et al (2020). The advantages of the smooth
nonparametric approach w.r.t. the parametric approach, using three parametric
distributions (Weibull, log-normal and gamma) in Backer et al (2020) is
discussed.
  It is shown that the typical rate of convergence of the smooth estimate of
the density is $n^{2/7}$ in a continuous version of the model, where $n$ is the
sample size. The (non-smoothed) nonparametric maximum likelihood estimator
(MLE) itself is computed by the iterative convex minorant algorithm (Groeneboom
and Jongbloed (2014)). All computations are available as {\tt R} scripts in
Groeneboom (2020).
