Developing an integrated approach to assess the emergence threat associated with influenza D viruses’ circulating in Europe
Bibliographic record
Abstract
Recent studies have identified a new genus of the Orthomyxoviridae family, Influenza D virus (IDV). This virus was shown to infect farm animals including swine and cattle, and to efficiently replicate and transmit in ferrets (Hause et al., 2013; Liu et al., 2020), the animal model of choice for transmission of influenza A virus to humans. This partnering grant (Flu-D project) on IDV addressed the need for capacity building at EU level to improve the EU's scientific assessment capacity and international competitiveness. We have promoted cross-disciplinary cooperation between the partner institutes representing six Member States (BE, FR, IE, IT, LU, and SE). We have shown that the available antibody testing methods allow reliable influenza D diagnostics in partners’ laboratories but that molecular diagnostic systems required some primers/protocols adjustments. Serological results in European cattle suggest that influenza D virus is enzootic and antigenic maps generated with reference antisera showed 2 main antigenic clusters, matching the genetic clustering. Virus diversity is still unfolding with new virus introductions identified, as well as the discovery of new reassortants whose differential clinical impact or cross-protection levels are still poorly understood. A quantitative risk assessment model (QRAM) of IDV through introduction of cattle in a country or a herd was developed and refined including several mitigation measures (e.g. testing strategy, vaccination/biosecurity). Complementary, an innovative tool that estimated the biosecurity level of protection of a farm was developed and pre-tested. The present project led to ideas and data sharing, cross-consortium training, and to the sustainability of our influenza D network in Europe with perspectives on future collaborative projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".