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Record W2149193607 · doi:10.1128/jvi.01573-15

Evolutionary Dynamics and Global Diversity of Influenza A Virus

2015· article· en· W2149193607 on OpenAlexaboutno aff
Daniel Rejmanek, Parviez R. Hosseini, Jonna A. K. Mazet, Peter Daszak, Tracey Goldstein

Bibliographic record

VenueJournal of Virology · 2015
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyNonsynonymous substitutionInfluenza A virusInfluenza A virus subtype H5N1Global healthPandemicDiversity (politics)PhylogeneticsEvolutionary biologyPublic healthDemographyGeneticsGenomeCoronavirus disease 2019 (COVID-19)Virus

Abstract

fetched live from OpenAlex

UNLABELLED: The increasing number of zoonotic infections caused by influenza A virus (IAV) subtypes of avian origin (e.g., H5N1 and H7N9) in recent years underscores the need to better understand the factors driving IAV evolution and diversity. To evaluate the current feasibility of global analyses to contribute to this aim, we evaluated information in the public domain to explore IAV evolutionary dynamics, including nucleotide substitution rates and selection pressures, using 14 IAV subtypes in 32 different countries over a 12-year period (2000 to 2011). Using geospatial information from 39,785 IAV strains, we examined associations between subtype diversity and socioeconomic, biodiversity, and agricultural indices. Our analyses showed that nucleotide substitution rates for 11 of the 14 evaluated subtypes tended to be higher in Asian countries, particularly in East Asia, than in Canada and the United States. Similarly, at a regional level, subtypes H5N1, H5N2, and H6N2 exhibited significantly higher substitution rates in East Asia than in North America. In contrast, the selection pressures (measured as ratios of nonsynonymous to synonymous evolutionary changes [dN/dS ratios]) acting on individual subtypes showed little geographic variation. We found that the strongest predictors for the detected subtype diversity at the country level were reporting effort (i.e., total number of strains reported) and health care spending (an indicator of economic development). Our analyses also identified major global gaps in IAV reporting (including a lack of sequences submitted from large portions of Africa and South America and a lack of geolocation information) and in broad subtype testing which, until addressed, will continue to hinder efforts to track the evolution and diversity of IAV around the world. IMPORTANCE: In recent years, an increasing number of influenza A virus (IAV) subtypes, including H5N1, H7N9, and H10N8, have been detected in humans. High fatality rates have led to an increased urgency to better understand where and how novel pathogenic influenza virus strains emerge. Our findings showed that mutational rates of 11 commonly encountered subtypes were higher in East Asian countries than in North America, suggesting that there may be a greater risk for the emergence of novel pathogenic strains in East Asia. In assessing the potential drivers of IAV subtype diversity, our analyses confirmed that reporting effort and health care spending were the best predictors of the observed subtype diversity at the country level. These findings underscore the need to increase sampling and reporting efforts for all subtypes in many undersampled countries throughout the world.

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 score0.004
Threshold uncertainty score0.231

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 head0.089
GPT teacher head0.383
Teacher spread0.294 · 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

Citations61
Published2015
Admission routes1
Has abstractyes

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