Authors
Christopher JR 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
Publication date
2022/3/1
Journal
Molecular biology and evolution
Volume
39
Issue
3
Pages
msac025
Publisher
Oxford University Press
Description
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 …
Scholar articles
CJR Illingworth, WL Hamilton, C Jackson, B Warne… - Molecular biology and evolution, 2022
CJR Illingworth, WL Hamilton, C Jackson, A Popay… - MedRxiv, 2020