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Record W4411306022 · doi:10.1101/2025.06.12.25329406

Reconstruction of SARS-CoV-2 transmissibility within households in the UK Virus Watch Study

2025· preprint· en· W4411306022 on OpenAlexaff
Laura Buggiotti, Arturo Torres Ortiz, Xavier Didelot, Alexei Yavlinsky, Cyril Geismar, Jana Kovar, Charles M. Miller, Luz Marina Martin Bernal, Rachel Williams, Ibrahim Abubakar, Robert W Aldridge, Judith Breuer

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilEuropean CommissionWellcome Trust
KeywordsTransmissibility (structural dynamics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakVirologySars virusVirusMedicineOutbreakPhysicsInternal medicineDiseaseAcoustics

Abstract

fetched live from OpenAlex

Households provide ideal settings to study SARS-CoV-2 transmission due to close proximity and extended exposure among members. Understanding transmission patterns is crucial for implementing effective control measures. We analysed whole genome sequences from 237 subjects across 162 households within the Virus Watch Prospective Community Cohort Study (372 total participants). We incorporated minority variants as indicators of within-host genomic diversity into phylogenetic models to reconstruct viral relationships more accurately than using consensus sequences alone. In 73/162 households with multiple infections, phylogeny identified eight secondary cases resulting from separate introduction events rather than within-household transmission. For the remaining 65 households, including minority variants resolved transmission chains that were otherwise uncertain. Our approach demonstrates that integrating within-host genetic diversity into phylogenetic models alongside epidemiological data provides a robust method to accurately identify household transmission events, assess infection risk factors, and improve secondary attack rate estimation, essential for understanding viral spread dynamics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.350
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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