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Record W3009119586 · doi:10.1101/2020.03.02.20030049

Estimation of COVID-19 outbreak size in Italy based on international case exportations

2020· preprint· en· W3009119586 on OpenAlexaff
Ashleigh R. Tuite, Victoria Ng, Erin E. Rees, David N. Fisman

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsPublic Health Agency of CanadaUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)OutbreakGeography2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EstimationSocioeconomicsMedicineVirologyEconomicsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Italy is currently experiencing an epidemic of COVID-19 which emerged in the Lombardy region 1 . During the interval between February 25-29, 2020, we identified 46 cases of COVID-19 reported in 21 countries in Europe, Africa, North America, and South America which were either in individuals with recent travel from Italy, or who had presumed infection by a traveler from Italy 2 . In six cases, in four of the affected countries (Switzerland, France, Austria, Croatia), land travel was a likely route of introduction, or was documented to have been the route of introduction 2 .

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)0.001
metaresearch head score (Gemma)0.098
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.098
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.266
GPT teacher head0.454
Teacher spread0.188 · 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 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

Citations26
Published2020
Admission routes1
Has abstractyes

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