Genomics-informed outbreak investigations of SARS-CoV-2 using civet.

Áine O'Toole, Verity Hill, Ben Jackson, Rebecca Dewar, Nikita Sahadeo, Rachel Colquhoun, Stefan Rooke, J T McCrone, Kate Duggan, Martin P McHugh, Samuel M Nicholls, Radoslaw Poplawski, COVID-19 Genomics UK (COG-UK) Consortium, COVID-19 Impact Project (Trinidad & Tobago Group), David Aanensen, Matt Holden, Tom Connor, Nick Loman, Ian Goodfellow, Christine V F Carrington, Kate Templeton, Andrew Rambaut

PLOS global public health 2022

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The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different 'catchments' and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health. Copyright: © 2022 O’Toole et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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PMID: 36962792

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