Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.

Details

Ressource 1Download: 40233303_BIB_90B0681CC1AD.pdf (544.38 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_90B0681CC1AD
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.
Journal
PLoS computational biology
Author(s)
Hodcroft E.B., Wohlfender M.S., Neher R.A., Riou J., Althaus C.L.
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Publication state
Published
Issued date
04/2025
Peer-reviewed
Oui
Volume
21
Number
4
Pages
e1012960
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.
Keywords
COVID-19/epidemiology, COVID-19/transmission, COVID-19/virology, SARS-CoV-2/genetics, Humans, Bayes Theorem, Genome, Viral, Computational Biology, Pandemics, Basic Reproduction Number/statistics & numerical data, Computer Simulation, Cluster Analysis, Germany/epidemiology, Switzerland/epidemiology, Mutation Rate, Mutation
Pubmed
Web of science
Open Access
Yes
Create date
28/04/2025 15:24
Last modification date
14/07/2025 11:53
Usage data