A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events | Zendy

Christopher J. R. Illingworth | Zendy; William L. Hamilton | Zendy; Christopher Jackson | Zendy; Ben Warne | Zendy; A.I. Popay | Zendy; Luke Meredith | Zendy; Myra Hosmillo | Zendy; Aminu S. Jahun | Zendy; Tom Fieldman | Zendy; Matthew Routledge | Zendy; Charlotte J. Houldcroft | Zendy; Laura Caller | Zendy; Sarah Caddy | Zendy; Anna Yakovleva | Zendy; Grant Hall | Zendy; Fahad Khokhar | Zendy; Theresa Feltwell | Zendy; Malte L. Pinckert | Zendy; Iliana Georgana | Zendy; Yasmin Chaudhry | Zendy; Martin D. Curran | Zendy; Surendra Parmar | Zendy; Dominic Sparkes | Zendy; Lucy Rivett | Zendy; Nick K Jones | Zendy; Sushmita Sridhar | Zendy; Stephen R. Forrest | Zendy; Tom Dymond | Zendy; Kayleigh Grainger | Zendy; Christopher T. Workman | Zendy; Effrossyni GkraniaKlotsas | Zendy; Nicholas M. Brown | Zendy; Michael P. Weekes | Zendy; Stephen Baker | Zendy; Sharon J. Peacock | Zendy; Theodore Gouliouris | Zendy; Ian Goodfellow | Zendy; Daniela De Angelis | Zendy; M. Estée Török | Zendy

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A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events

Author(s) -

Christopher J. R. Illingworth,

William L. Hamilton,

Christopher Jackson,

Ben Warne,

A.I. Popay,

Luke Meredith,

Myra Hosmillo,

Aminu S. Jahun,

Tom Fieldman,

Matthew Routledge,

Charlotte J. Houldcroft,

Laura Caller,

Sarah Caddy,

Anna Yakovleva,

Grant Hall,

Fahad Khokhar,

Theresa Feltwell,

Malte L. Pinckert,

Iliana Georgana,

Yasmin Chaudhry,

Martin D. Curran,

Surendra Parmar,

Dominic Sparkes,

Lucy Rivett,

Nick K Jones,

Sushmita Sridhar,

Stephen R. Forrest,

Tom Dymond,

Kayleigh Grainger,

Christopher T. Workman,

Effrossyni GkraniaKlotsas,

Nicholas M. Brown,

Michael P. Weekes,

Stephen Baker,

Sharon J. Peacock,

Theodore Gouliouris,

Ian Goodfellow,

Daniela De Angelis,

M. Estée Török

Publication year - 2022

Publication title -

molecular biology and evolution

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.637

H-Index - 218

eISSN - 1537-1719

pISSN - 0737-4038

DOI - 10.1093/molbev/msac025

Subject(s) - pandemic , context (archaeology) , transmission (telecommunications) , covid-19 , biology , genome , identification (biology) , linkage (software) , virology , computational biology , infectious disease (medical specialty) , disease , genetics , medicine , computer science , pathology , gene , paleontology , telecommunications , botany

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.

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