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Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 with grinch

2021· preprint· en· W3200130214 on OpenAlexafffund
Áine O’Toole, Verity Hill, Oliver G. Pybus, Alexander Watts, Isaac I. Bogoch, Kamran Khan, Jane P. Messina, Houriiyah Tegally, Richard Lessells, Jennifer Giandhari, Sureshnee Pillay, Kefentse A. Tumedi, Naledi Gape Nyepetsi, Malebogo Kebabonye, Maitshwarelo Matsheka, Madisa Mine, Sima Tokajian, Hamad Hassan, Tamara Salloum, Georgi Merhi, Jad Koweyes, Jemma L. Geoghegan, Joep de Ligt, Xiaoyun Ren, Matthew Storey, Nikki E. Freed, Chitra Pattabiraman, Pramada Prasad, Anita Desai, Vasanthapuram Ravi, Thomas F. Schulz, Lars Steinbrück, Tanja Stadler, Antonio Parisi, Angelica Bianco, Darı́o Garcı́a de Viedma, Sergio Buenestado‐Serrano, Vítor Borges, Joana Isidro, Sílvia Duarte, João Paulo Gomes, Neta S. Zuckerman, Michal Mandelboim, Orna Mor, Torsten Seemann, Alicia Arnott, Jenny Draper, Mailie Gall, William D. Rawlinson, Ira W. Deveson, Sanmarié Schlebusch, Jamie McMahon, Lex E.X. Leong, Chuan Kok Lim, Maria Chironna, Daniela Loconsole, Antonin Bal, Laurence Josset, Edward C. Holmes, Kirsten St. George, Erica Lasek‐Nesselquist, Reina S. Sikkema, Bas B. Oude Munnink, Marion Koopmans, Mia Brytting, Vannavada Sudha Rani, S. Pavani, Teemu Smura, Albert Heim, Satu Kurkela, Massab Umair, Muhammad Salman, Barbara Bartolini, Martina Rueca, Christian Drosten, Thorsten Wolff, Olin Silander, Dirk Eggink, Chantal Reusken, Harry Vennema, Aekyung Park, Christine V. F. Carrington, Nikita Sahadeo, Michael J. Carr, Gabo Gonzalez, Túlio de Oliveira, Nuno R. Faria, Andrew Rambaut, Moritz U. G. Kraemer

Bibliographic record

VenueWellcome Open Research · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of TorontoUniversity Health NetworkBlueDot (Canada)St. Michael's Hospital
FundersEuropean Research CouncilInstituto de Salud Carlos IIIBiotechnology and Biological Sciences Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchFast GrantsDirectorate for Biological SciencesSouth African Medical Research CouncilBranco Weiss Fellowship – Society in ScienceEuropean CommissionNational Institute for Health and Care ResearchWellcome TrustAcademy of Medical SciencesFundação de Amparo à Pesquisa do Estado de São PauloDeutsche ForschungsgemeinschaftUK Research and Innovation
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Lineage (genetic)HaplotypeCoronavirus disease 2019 (COVID-19)Biological dispersal2019-20 coronavirus outbreakTrack (disk drive)CoronavirusEvolutionary biologyBiologyTracking (education)GeneticsGeographyComputational biologyVirologyGeneMedicineComputer scienceEnvironmental healthSociology

Abstract

fetched live from OpenAlex

Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.243
GPT teacher head0.467
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations228
Published2021
Admission routes2
Has abstractyes

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