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Record W2553154513 · doi:10.7554/elife.18491

Viral factors in influenza pandemic risk assessment

2016· article· en· W2553154513 on OpenAlexafffund
Marc Lipsitch, William Barclay, Rahul Raman, Charles J. Russell, Jessica A. Belser, Sarah Cobey, Peter M. Kasson, James O. Lloyd‐Smith, Sebastian Maurer‐Stroh, Steven Riley, Catherine A. A. Beauchemin, Trevor Bedford, Thomas C. Friedrich, Andreas Handel, Sander Herfst, Pablo R. Murcia, Benjamín Roche, Claus O. Wilke, Colin A. Russell

Bibliographic record

VenueeLife · 2016
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsToronto Metropolitan University
FundersFogarty International CenterNational Health and Medical Research CouncilNational Institute of Allergy and Infectious DiseasesMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaScience and Technology DirectorateCenters for Disease Control and PreventionAgency for Science, Technology and ResearchU.S. Department of Homeland SecurityRoyal SocietyNational Institute of General Medical SciencesNational Institute for Health and Care ResearchNederlandse Organisatie voor Wetenschappelijk OnderzoekWellcome TrustNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsPandemicHemagglutinin (influenza)VirologyBiologyVirusInfluenza A virusTransmission (telecommunications)OrthomyxoviridaeCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)MedicineDisease

Abstract

fetched live from OpenAlex

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.105
GPT teacher head0.437
Teacher spread0.332 · 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 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

Citations115
Published2016
Admission routes2
Has abstractyes

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