Benjamin Sobkowiak1, Kimia Kamelian2, James E A Zlosnik3, John Tyson3, Anders Gonçalves da Silva4, Linda M N Hoang3,5, Natalie Prystajecky3,5, Caroline Colijn6. 1. Department of Mathematics, Simon Fraser University, Burnaby, Canada. benjamin_sobkowiak@sfu.ca. 2. Public Health Agency of Canada, National Microbiology Laboratory, Winnipeg, MB,, Canada. 3. BC Centre for Disease Control Public Health Laboratory, BC Centre for Disease Control, Vancouver, Canada. 4. Department of Microbiology and Immunology Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne, Melbourne, Australia. 5. Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada. 6. Department of Mathematics, Simon Fraser University, Burnaby, Canada.
Abstract
BACKGROUND: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. RESULTS: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. CONCLUSIONS: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.
BACKGROUND: The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. RESULTS: We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. CONCLUSIONS: Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.
Authors: Louis du Plessis; John T McCrone; Alexander E Zarebski; Verity Hill; Christopher Ruis; Moritz U G Kraemer; Andrew Rambaut; Oliver G Pybus; Bernardo Gutierrez; Jayna Raghwani; Jordan Ashworth; Rachel Colquhoun; Thomas R Connor; Nuno R Faria; Ben Jackson; Nicholas J Loman; Áine O'Toole; Samuel M Nicholls; Kris V Parag; Emily Scher; Tetyana I Vasylyeva; Erik M Volz; Alexander Watts; Isaac I Bogoch; Kamran Khan; David M Aanensen Journal: Science Date: 2021-01-08 Impact factor: 47.728
Authors: Katia J Bruxvoort; Lina S Sy; Lei Qian; Bradley K Ackerson; Yi Luo; Gina S Lee; Yun Tian; Ana Florea; Michael Aragones; Julia E Tubert; Harpreet S Takhar; Jennifer H Ku; Yamuna D Paila; Carla A Talarico; Hung Fu Tseng Journal: BMJ Date: 2021-12-15
Authors: Ravi Kant; Phuoc Truong Nguyen; Soile Blomqvist; Mert Erdin; Hussein Alburkat; Maija Suvanto; Fathiah Zakham; Veera Salminen; Viktor Olander; Minna Paloniemi; Leena Huhti; Sara Lehtinen; Bruno Luukinen; Hanna Jarva; Hannimari Kallio-Kokko; Satu Kurkela; Maija Lappalainen; Hanna Liimatainen; Sari Hannula; Jani Halkilahti; Jonna Ikonen; Niina Ikonen; Otto Helve; Marianne Gunell; Tytti Vuorinen; Ilya Plyusnin; Erika Lindh; Pekka Ellonen; Tarja Sironen; Carita Savolainen-Kopra; Teemu Smura; Olli Vapalahti Journal: Emerg Infect Dis Date: 2021-10-28 Impact factor: 6.883