Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.
|Number of pages||14|
|Journal||Molecular Biology and Evolution|
|Early online date||2 Feb 2022|
|Publication status||Published - 2 Mar 2022|
Bibliographical noteFunding Information: This work was funded by COG-UK, which 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; We also acknowledge the support from the Wellcome [Senior Clinical Fellowship to M.P.W. (ref.: 108070/Z/15/Z), Senior Research Fellowship to S.B. (ref.: 215515/Z/19/Z), Senior Fellowship to I.G. (ref.: 207498/Z/17/Z); Collaborative Grant to C.J.H. (ref.: 204870/Z/16/Z); the Academy of Medical Sciences & the Health Foundation (Clinician Scientist Fellowship to M.E.T.), the NIHR Cambridge Biomedical Research Centre (to B.W., M.E.T.) and the NIHR Clinical Research Network Greenshoots award (to E.G.K.). C.J.R.I. was supported by Deutsche Forschungsgemeinschaft (DFG) Grant SFB 1310 and by UKRI through the JUNIPER modeling consortium [grant number MR/V038613/1]. We acknowledge UKRI Medical Research Council funding (Unit Programme numbers MC_UU_00002/11 and MC_UU_12014); NIHR Health Protection Units in Behavioural Science and Evaluation.
Open Access: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Copyright: © The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Citation: Christopher J R Illingworth, William L Hamilton, Christopher Jackson, Ben Warne, Ashley Popay, Luke Meredith, Myra Hosmillo, Aminu Jahun, Tom Fieldman, Matthew Routledge, Charlotte J Houldcroft, Laura Caller, Sarah Caddy, Anna Yakovleva, Grant Hall, Fahad A Khokhar, Theresa Feltwell, Malte L Pinckert, Iliana Georgana, Yasmin Chaudhry, Martin Curran, Surendra Parmar, Dominic Sparkes, Lucy Rivett, Nick K Jones, Sushmita Sridhar, Sally Forrest, Tom Dymond, Kayleigh Grainger, Chris Workman, Effrossyni Gkrania-Klotsas, Nicholas M Brown, Michael P Weekes, Stephen Baker, Sharon J Peacock, Theodore Gouliouris, Ian Goodfellow, Daniela De Angelis, M Estée Török, A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events, Molecular Biology and Evolution, Volume 39, Issue 3, March 2022, msac025,