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Record W3029057613 · doi:10.1002/jmv.26041

Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID‐19: Meta‐analysis and sensitivity analysis

2020· review· en· W3029057613 on OpenAlexafffund
Wenqing He, Grace Y. Yi, Yayuan Zhu

Bibliographic record

VenueJournal of Medical Virology · 2020
Typereview
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsActuaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAsymptomaticCase fatality rateIncubation periodCoronavirus disease 2019 (COVID-19)MedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Basic reproduction numberVirologyIncubationDiseaseBiologyInternal medicineInfectious disease (medical specialty)EpidemiologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

The coronavirus disease-2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscript conducts a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41-3.90), the average incubation time to be 5.08 days with the 95% CI (4.77-5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%-73.60%), and the case fatality rate to be 2.72% with 95% CI (1.29%-4.16%) where asymptomatic infections are accounted for.

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)0.019
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.037
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0120.049
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.223
GPT teacher head0.474
Teacher spread0.250 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations217
Published2020
Admission routes2
Has abstractyes

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