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Record W3024457609 · doi:10.1016/j.ijid.2020.05.046

Tracking the origin of early COVID-19 cases in Canada

2020· article· en· W3024457609 on OpenAlexafffundabout
Naizhuo Zhao, Ying Liu, Audrey Smargiassi, Sasha Bernatsky

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsMcGill UniversityUniversité de MontréalMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsCoronavirus disease 2019 (COVID-19)ChinaOutbreakPandemicGeography2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Tracking (education)DemographyDiseaseMedicineVirologyInfectious disease (medical specialty)SociologyArchaeology

Abstract

fetched live from OpenAlex

The original coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China has become a global pandemic. By tracking the earliest 118 COVID-19 cases in Canada, we produced a Voronoi treemap to show the travel origins of the country's earliest COVID-19 cases. By March 11, 2020, even though the majority (64.1%) of the world's COVID-19 confirmed cases still had their origin in China, only 7.6% of Canada's first 118 COVID-19 cases were related to travelers from China. The most commonly reported travel history among the 118 cases related to the Middle East, the United States, and Europe. Thus, in retrospect, broadening of early screening tools and travel restrictions to countries and regions outside China may have helped control global COVID-19 spread.

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)0.000
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.187
GPT teacher head0.419
Teacher spread0.232 · 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 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

Citations22
Published2020
Admission routes3
Has abstractyes

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