TY - JOUR
AU - Illingworth, Christopher J.R.
AU - Hamilton, William L.
AU - Jackson, Chris
AU - Warne, Ben
AU - Popay, Ashley
AU - Meredith, Luke W.
AU - Hosmillo, Myra
AU - Jahun, Aminu S.
AU - Fieldman, Tom
AU - Routledge, Matthew
AU - Houldcroft, Charlotte J.
AU - Caller, Laura G.
AU - Caddy, Sarah L.
AU - Yakovleva, Anna
AU - Hall, Grant
AU - Khokhar, Fahad A.
AU - Feltwell, Theresa
AU - Pinckert, Malte L.
AU - Georgana, Iliana
AU - Chaudhry, Yasmin
AU - Curran, Martin D.
AU - Parmar, Surendra
AU - Sparkes, Dominic
AU - Rivett, Lucy
AU - Jones, Nick K.
AU - Sridhar, Sushmita
AU - Forrest, Sally
AU - Dymond, Tom
AU - Grainger, Kayleigh
AU - Workman, Chris
AU - Gkrania-Klotsas, Effrossyni
AU - Brown, Nicholas M.
AU - Weekes, Michael P.
AU - Baker, Stephen
AU - Peacock, Sharon J.
AU - Gouliouris, Theodore
AU - Goodfellow, Ian G.
AU - De Angelis, Daniela
AU - Török, M. Estée
PY - 2022
DA - February
TI - A2B-COVID: a tool for rapidly evaluating potential SARS-CoV-2 transmission events
JO - Molecular Biology and Evolution
DO - http://dx.doi.org/10.1093/molbev/msac025
AB - 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 (CUH) 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.
PB - Oxford University Press
UR - http://eprints.gla.ac.uk/263770/
KW - Coronavirus (COVID-19)
ER