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Record W4297235703 · doi:10.1101/2022.09.24.22280269

Emergence and spread of two SARS-CoV-2 variants of interest in Nigeria

2022· preprint· en· W4297235703 on OpenAlexaff
Idowu B. Olawoye, Paul E. Oluniyi, Judith U. Oguzie, Jessica N. Uwanibe, Adeyemi T. Kayode, Testimony Olumade, Fehintola V. Ajogbasile, Edyth Parker, Philomena Eromon, Priscilla Abechi, Tope Sobajo, Chinedu Ugwu, Femi Ayoade, Kazeem Akano, Nicholas Oyejide, Iyanuoluwa Fred-Akintunwa, Adedotun-Sulaiman Kemi, Farida Oluwabukola Brimmo, Benjamin Adegboyega, Courage Philip, Ayomide Adeleke, Grace C. Chukwu, Muhammad I. Ahmed, Oludayo O. Ope-ewe, Shobi G. Otitoola, Olusola Ogunsanya, Mudasiru F. Saibu, Ayotunde E. Sijuwola, Grace O. Ezekiel, Oluwagboadurami G. John, Julie O. Akin-John, Oluwasemilogo O. Akinlo, Olanrewaju O. Fayemi, Testimony O. Ipaye, Deborah C. Nwodo, Abolade E. Omoniyi, Iyobosa B. Omwanghe, Christabel A. Terkuma, Johnson Okolie, Olubukola Ayo-Ale, Ikponmwosa Odia, Benevolence Ebo, Okonofua Grace Naregose, Patience Akhilomen, Osiemi Blessing, Airende Micheal, Jacqueline Agbukor, Aiyepada John, Paulson Ebhodaghe, Rita Esumeh, Giwa Rosemary, Solomon Ehikhametalor, Anieno Ekanem, Yerumoh Edna, Aire O. Chris, Adomeh Donatus, Ephraim Ogbaini, Mirabeau Youtchou Tatfeng, Hannah E. Omunakwe, Mienye Bob-Manuel, Rahaman A. Ahmed, Chika Onwuamah, Joseph Ojonugwa Shaibu, Azuka Patrick Okwuraiwe, Anthony E. Atage, Andrew Bock-Oruma, Funmi Daramola, Akinwumi Fajola, Nsikak-Abasi Ntia, Anietie Moses, Worbianueri B. Moore-Igwe, Ibrahim F. Yusuf, Enoch O. Fakayode, Monilade Akinola, Ibrahim Musa Kida, Bamidele Soji Oderinde, Zara Wudiri, Olufemi O. Adeyemi, Olusola Anuoluwapo Akanbi, Anthony Ahumibe, Afolabi Akinpelu, Oyeronke Ayansola, Olajumoke Babatunde, Adesuyi Ayodeji Omoare, Chimaobi Chukwu, Nwando Mba, Ewean Chukwuma Omoruyi, Johnson Adekunle Adeniji, Moses O. Adewunmi, Oluseyi A. Olayinka, Olisa Olasunkanmi, Olatunji K. Akande, Ifeanyi Emmanuel Nwafor, Matthew A. Ekeh, Erim Ndoma, Richard L. Ewah, Rosemary O. Duruihuoma, Augustine Abu, Elizabeth Odeh, Venatious Onyia, Kingsley Chiedozie Ojide, Sylvanus Okoro, Daniel Igwe, Kamran Khan, Anthony N. Ajayi, Ebhodaghe Ngozi Ugwu, Collins Ugwu, Kingsley Ukwuaja, Emeka Onwe Ogah, Chukwuyem Abejegah, Nelson Adedosu, Olufemi Ayodeji, Rafiu O. Isamotu, Galadima Gadzama, Brittany A. Petros, Katherine J. Siddle, S. F. Schaffner, George O. Akpede, Cyril Erameh, Baba M, Femi Oladiji, Rosemary Audu, Nnaemeka Ndodo, Adeola Fowotade, Okogbenin Sylvanus, Peter O. Okokhere, Bronwyn Mcannis, Ifedayo Adetifa, Chikwe Ihekweazu, Babatunde Lawal Salako, Oyewale Tomori, Anise N. Happi, Onikepe Folarin, Kristian G. Andersen, Pardis C. Sabeti, Christian Happi

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsBlueDot (Canada)
FundersCenters for Disease Control and PreventionNational Institutes of HealthELMA FoundationNational Institute of Allergy and Infectious DiseasesSkoll FoundationOpen Philanthropy ProjectWorld Bank GroupWellcome TrustRockefeller Foundation
KeywordsPandemicPublic healthGeographyPhylogeographyTransmission (telecommunications)Order (exchange)Coronavirus disease 2019 (COVID-19)Economic geographyBiologyDevelopment economicsRegional scienceBusinessPhylogeneticsEconomicsComputer scienceTelecommunicationsGeneGeneticsDiseaseMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the B.1.1.318 and B.1.525 variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Our results show how regional connectivity in downsampled regions like Africa can often influence virus transmissions between neighbouring countries. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission in the region, generating actionable information for public health decision makers in the region.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.426
GPT teacher head0.460
Teacher spread0.034 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venuemedRxiv→Same topicCOVID-19 epidemiological studies→French-language works237,207→