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A2B-COVID: a tool for rapidly evaluating potential SARS-CoV-2 transmission events

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Τίτλος: A2B-COVID: a tool for rapidly evaluating potential SARS-CoV-2 transmission events
Συγγραφείς: Illingworth, CJR, Hamilton, WL, Jackson, C, Warne, B, Popay, A, Meredith, L, Hosmillo, M, Jahun, A, Fieldman, T, Routledge, M, Houldcroft, CJ, Caller, L, Caddy, S, Yakovleva, A, Hall, G, Khokhar, FA, Feltwell, T, Pinckert, ML, Georgana, I, Chaudhry, Y, Curran, M, Parmar, S, Sparkes, D, Rivett, L, Jones, NK, Sridhar, S, Forrest, S, Dymond, T, Grainger, K, Workman, C, Gkrania-Klotsas, E, Brown, NM, Weekes, MP, Baker, S, Peacock, SJ, Gouliouris, T, Goodfellow, I, Angelis, DD, Török, ME
Στοιχεία εκδότη: Oxford University Press
Έτος έκδοσης: 2022
Συλλογή: Oxford University Research Archive (ORA)
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://doi.org/10.1093/molbev/msac025
DOI: 10.1093/molbev/msac025
Διαθεσιμότητα: https://doi.org/10.1093/molbev/msac025
https://ora.ox.ac.uk/objects/uuid:239c8885-af49-4e21-adc2-50dea7ea3ddc
Rights: info:eu-repo/semantics/openAccess ; CC Attribution (CC BY)
Αριθμός Καταχώρησης: edsbas.9DDB25AF
Βάση Δεδομένων: BASE
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  Data: 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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