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Record W3103817688 · doi:10.1101/2020.11.17.20232827

Combined epidemiological and genomic analysis of nosocomial SARS-CoV-2 transmission identifies community social distancing as the dominant intervention reducing outbreaks

2020· preprint· en· W3103817688 on OpenAlexaff
Luke B. Snell, Chloe L. Fisher, Usman Taj, Blair Merrick, Adela Alcolea-Medina, Themoula Charalampous, Adrian W. Signell, Harry Wilson, Gilberto Betancor, Mark Tan Kia Ik, Emma Cunningham, Penelope R. Cliff, Suzanne Pickering, Rui Pedro Galão, Rahul Batra, Stuart J. D. Neil, Michael H. Malim, Katie J. Doores, Sam Douthwaite, Gaia Nebbia, Jonathan D. Edgeworth, Ali Raza Awan

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsSt. Thomas Hospital
FundersWarwick Medical SchoolMenzies Centre for Australian Studies, King's College London, University of LondonUniversity Hospitals Coventry and Warwickshire NHS TrustCambridge University HospitalsUniversity of BrightonImperial College LondonDirectorate for Biological SciencesUniversity of WarwickUniversity of CambridgeLondon School of Hygiene and Tropical MedicineKing's College LondonUniversity of Portsmouth
KeywordsEpidemiologyPsychological interventionOutbreakMedicineTransmission (telecommunications)Social distancePandemicInfection controlHealth careIntervention (counseling)DiseaseIntensive care medicineVirologyInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Internal medicineNursing

Abstract

fetched live from OpenAlex

ABSTRACT Many healthcare facilities report SARS-CoV-2 outbreaks but transmission analysis is complicated by the high prevalence of infection and limited viral genetic diversity. The contribution of different vectors to nosocomial infection or the effectiveness of interventions is therefore currently unclear. Detailed epidemiological and viral nanopore sequence data were analysed from 574 consecutive patients with a PCR positive SARS-CoV-2 test between March 13th and March 31st, when the pandemic first impacted on a large, multisite healthcare institution in London. During this time the first major preventative interventions were introduced, including progressive community social distancing (CSD) policies leading to mandatory national lockdown, exclusion of hospital visitors, and introduction of universal surgical facemask-use by healthcare-workers (HCW). Incidence of nosocomial cases, community SARS-CoV-2 cases and infection in a cohort of 228 HCWs followed the same dynamic course, decreasing shortly after introduction of CSD measures and prior to the main hospital-based interventions. We investigated clusters involving nosocomial cases based on overlapping ward-stays during the 14-day incubation period and SARS-CoV-2 genome sequence similarity. Our method placed 63 (79%) of 80 sequenced probable and definite nosocomial cases into 14 clusters containing a median of 4 patients (min 2, max 19) No genetic support was found for the majority of epidemiological clusters (31/44, 70%) and genomics revealed multiple contemporaneous outbreaks within single epidemiological clusters. We included a measure of hospital enrichment compared to community cases to increase confidence in our clusters, which were 1-14 fold enriched. Applying genomics, we could provide a robust estimate of the incubation period for nosocomial transmission, with a median lower bound and upper bound of 6 and 9 days respectively. Six (43%) clusters spanned multiple wards, with evidence of cryptic transmission, and community-onset cases could not be identified in more than half the clusters, particularly on the elective hospital site, implicating HCW as vectors of transmission. Taken together these findings suggest that CSD had the dominant impact on reducing nosocomial transmission by reducing HCW infection.

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 score0.003
Threshold uncertainty score0.006

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.266
GPT teacher head0.441
Teacher spread0.176 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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