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Record W3025300857 · doi:10.1049/ccs.2020.0017

Predicting COVID‐19 trends in Canada: a tale of four models

2020· article· en· W3025300857 on OpenAlexaffabout
Wandong Zhang, W. G. Will Zhao, Dana Wu, Yimin Yang

Bibliographic record

VenueCognitive Computation and Systems · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsCanada Research ChairsUniversity of TorontoLakehead UniversityUniversity of Windsor
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Logistic regressionPandemicEconometrics2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Turning pointRegressionRegression analysisDemographyStatisticsGeographyComputer scienceMedicineEconomicsMathematicsVirologyPeriod (music)Sociology

Abstract

fetched live from OpenAlex

This study aims to offer multiple‐model informed predictions of COVID‐19 in Canada, specifically through the use of deep learning strategy and mathematical prediction models including long‐short term memory network, logistic regression model, Gaussian model, and susceptible‐infected‐removed model. Using the published data as of the 10th of April 2020, the authors forecast that the daily increased number of infective cases in Canada has not reached the peak turning point and will continue to increase. Therefore, Canada's healthcare services need to be ready for the magnitude of this pandemic.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.754
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.116
GPT teacher head0.332
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2020
Admission routes2
Has abstractyes

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