Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/34696
Title: Quantifying superspreading for COVID-19 using Poisson mixture distributions
Authors: KREMER, Cécile 
TORNERI, Andrea 
Boesmans, Sien
MEUWISSEN, Hanne 
VERDONSCHOT, Selina 
VANDEN DRIESSCHE, Koen 
Althaus, Christian L
FAES, Christel 
HENS, Niel 
Issue Date: 2021
Publisher: NATURE RESEARCH
Source: Scientific reports (Nature Publishing Group), 11 (1) (Art N° 14107)
Abstract: The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
Keywords: COVID-19;Computer Simulation;Hong Kong;Humans;India;Infectious Disease Transmission, Vertical;Poisson Distribution;Rwanda;SARS-CoV-2
Document URI: http://hdl.handle.net/1942/34696
ISSN: 2045-2322
e-ISSN: 2045-2322
DOI: 10.1038/s41598-021-93578-x
ISI #: WOS:000674513600025
Rights: The Author(s) 2021. Open Access Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
s41598-021-93578-x.pdfPublished version3.49 MBAdobe PDFView/Open
Show full item record

WEB OF SCIENCETM
Citations

13
checked on Jul 10, 2024

Page view(s)

22
checked on Jun 27, 2022

Download(s)

8
checked on Jun 27, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.