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Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - a high risk community-hospital interface

Authors :
Joseph Hughes
Oliver Stirrup
Alison Taylor
Natasha Johnson
Kathy Li
Rory Gunson
James Shepherd
Josh Singer
Jennifer S Lees
Yasmin A Parr
Judith G Breuer
Aislynn Taggart
Timothy Willem Jones
Y. Mun Woo
David Robertson
Patrick B. Mark
Igor Starinskij
Vattipally B. Sreenu
Marc Niebel
E. Thomson
Elihu Aranday-Cortes
Scott T W Morris
Ana da Silva Filipe
Natasha Jesudason
Daniel Mair
Jamie P. Traynor
Rajiv Shah
Kyriaki Nomikou
Antonia Ho
Zoe Cousland
Kirstyn Brunker
Alasdair MacLean
Colin C. Geddes
Peter C. Thomson
Sarah E. McDonald
Stephen Carmichael
Jonathan Price
Jenna Nichols
Carlos Varon Lopez
Patawee Asamaphan
Lily Tong
Katherine Smollett
Mair, Daniel [0000-0001-7169-9080]
Nomikou, Kyriaki [0000-0002-7013-1853]
Niebel, Marc [0000-0003-2515-6151]
Shah, Rajiv [0000-0002-2827-5108]
Jones, Timothy PW [0000-0001-6147-6748]
Starinskij, Igor [0000-0001-8585-5929]
Mark, Patrick B [0000-0003-3387-2123]
Apollo - University of Cambridge Repository
Publication Year :
2021
Publisher :
Cold Spring Harbor Laboratory, 2021.

Abstract

ObjectivesPatients requiring haemodialysis are at increased risk of serious illness with SARS-CoV-2 infection. To improve the understanding of transmission risks in six Scottish renal dialysis units, we utilised the rapid whole-genome sequencing data generated by the COG-UK consortium.MethodsWe combined geographical, temporal and genomic sequence data from the community and hospital to estimate the probability of infection originating from within the dialysis unit, the hospital or the community using Bayesian statistical modelling and compared these results to the details of epidemiological investigations.ResultsOf 671 patients, 60 (8.9%) became infected with SARS-CoV-2, of whom 16 (27%) died. Within-unit and community transmission were both evident and an instance of transmission from the wider hospital setting was also demonstrated.ConclusionsNear-real-time SARS-CoV-2 sequencing data can facilitate tailored infection prevention and control measures, which can be targeted at reducing risk in these settings.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....0992531eeab2cbac9d0dd9e7213e6523