Genomic and healthcare dynamics of nosocomial SARS-CoV-2 transmission
- University of Manchester, United Kingdom
- Manchester University NHS Foundation Trust, United Kingdom
- Manchester Centre for Genomic Medicine, United Kingdom
Abstract
Understanding the effectiveness of infection control methods in reducing and preventing SARS-CoV-2 transmission in healthcare settings is of high importance. We sequenced SARS-CoV-2 genomes for patients and healthcare workers (HCWs) across multiple geographically distinct UK hospitals, obtaining 173 high-quality SARS-CoV-2 genomes. We integrated patient movement and staff location data into the analysis of viral genome data to understand spatial and temporal dynamics of SARS-CoV-2 transmission. We identified eight patient contact clusters (PCC) with significantly increased similarity in genomic variants compared to non-clustered samples. Incorporation of HCW location further increased the number of individuals within PCCs and identified additional links in SARS-CoV-2 transmission pathways. Patients within PCCs carried viruses more genetically identical to HCWs in the same ward location. SARS-CoV-2 genome sequencing integrated with patient and HCW movement data increases identification of outbreak clusters. This dynamic approach can support infection control management strategies within the healthcare setting.
Data availability
All genome sequencing datasets have been shared with COG-UK.
Article and author information
Author details
Jamie M Ellingford
Funding
Health Education England
- Jamie M Ellingford
Manchester Biomedical Research Centre
- William G Newman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was conducted to investigate hospital outbreak investigation/surveillance; individual patient consent or ethical approvals were not required. The study protocol was approved by the Manchester Biomedical Research Centre COVID-19 rapid response group and the Manchester University NHS Foundation Trust Executive Committee. All samples and data collected were part of routine care or hospital operational policy. No patient-identifiable/individual identifiable data are presented.
Copyright
© 2021, Ellingford et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Genomic and healthcare dynamics of nosocomial SARS-CoV-2 transmission
eLife 10:e65453.
https://doi.org/10.7554/eLife.65453
Further reading
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- Medicine
- Microbiology and Infectious Disease
- Epidemiology and Global Health
- Immunology and Inflammation
eLife has published articles on a wide range of infectious diseases, including COVID-19, influenza, tuberculosis, HIV/AIDS, malaria and typhoid fever.
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- Genetics and Genomics
Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes – most notably functional regulatory elements – in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants – at all scales, from other genetic variants and gene regulation to cell biology to organismal environment – are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.
