Library
local_library Home Repository

Estimation of the incubation time distribution for COVID-19

Journal Article (2020)
Author(s)

Piet Groeneboom (TU Delft - Statistics)

Research Group
Statistics
DOI related publication
https://doi.org/10.1111/stan.12231
More Info
expand_more
Publication Year
2020
Language
English
Research Group
Statistics
Issue number
2
Volume number
75
Pages (from-to)
161-179
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

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 with respect to 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 n2/7 in a continuous version of the model, where n is the sample size. The (nonsmoothed) nonparametric maximum likelihood estimator itself is computed by the iterative convex minorant algorithm (Groeneboom and Jongbloed (2014)). All computations are available as R scripts in Groeneboom (2020a).