Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data

eLife

Aleksandra Marek¹  Christine Peters¹  Francesc Coll²  Judith Breuer³  Oliver Stirrup⁴  Joseph Hughes⁵  James G Shepherd⁵  Joshua B Singer⁵  Emma C Thomson⁶  Asif Tamuri⁷  Matthew Parker⁸  Alexander Keeley⁹  David G Partridge⁹  Thushan I de Silva⁹  Benjamin B Lindsey⁹  James Blackstone^1,10 

[1] Clinical Microbiology, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom;Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom;Division of Infection and Immunity, University College London, London, United Kingdom;Institute for Global Health, University College London, London, United Kingdom;MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom;MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom;Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom;Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, United Kingdom;Research Computing, University College London, London, United Kingdom;Sheffield Bioinformatics Core, The University of Sheffield, Sheffield, United Kingdom;Sheffield Institute for Translational Neuroscience, The University of Sheffield, Sheffield, United Kingdom;Sheffield Biomedical Research Centre, The University of Sheffield, Sheffield, United Kingdom;Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom;The Florey Institute for Host-Pathogen Interactions & Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom;The Comprehensive Clinical Trials Unit at UCL , University College London, London, United Kingdom;

关键词: COVID-19;    healthcare associated;    nosocomial;    SARS-CoV-2;    whole genome sequencing;    outbreak;    Human;   

DOI  :  10.7554/eLife.65828

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Background:Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.Methods:We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February–May 2020.Results:We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3–7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).Conclusions:The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.Funding:COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.

【 授权许可】

CC BY