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

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Li, Kathy K 
Woo, Y Mun 
Stirrup, Oliver 
Hughes, Joseph 
Ho, Antonia 

Abstract

OBJECTIVES: Patients 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. METHODS: We 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. RESULTS: Of 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. CONCLUSIONS: Near-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.

Description

Keywords

COVID-19, Haemodialysis, Infection control, Nosocomial, Outbreak, Rapid sequencing, Renal dialysis unit, SARS-CoV-2, Bayes Theorem, COVID-19, Hospitals, Humans, Molecular Epidemiology, Renal Dialysis, SARS-CoV-2

Journal Title

J Infect

Conference Name

Journal ISSN

0163-4453
1532-2742

Volume Title

83

Publisher

Elsevier BV

Rights

All rights reserved
Sponsorship
MRC (MC_PC_19027)
UK Research and Innovation (MC_PC_19027)
COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute.