Assessment of seasonality of influenza in swine using field submissions to a diagnostic laboratory in Ontario between 2007 and 2012
Bibliographic record
Abstract
BACKGROUND: Seasonality of any infectious disease is important for its control and monitoring. While influenza seasonality in people has been evaluated extensively, this question has not been studied well in swine populations. OBJECTIVE: The goal of this study was to investigate seasonality of influenza in swine, using diagnostic submissions to a diagnostic laboratory. METHODS: Two thousand seven hundred and eleven virological tests within 685 submissions and 5471 serological tests within 193 submissions in Ontario swine between 2007 and 2012 were included in the study and converted to total monthly number of virological and serological submissions, and the number of positive submissions. Data were analyzed by time-series decomposition, fixed-effect Poisson, random-effect Poisson regression with month as uncorrelated and correlated random effects. RESULTS: All approaches identified seasonality in virological submissions (P < 0.02) with peak in January and April, and a trough in July, but were not able to detect seasonality of influenza-positive virological submissions (P > 0.13). Seasonality of positive serological submissions was identified only if independence between months was assumed (P < 0.03). Almost 50% of serological submissions had evidence of exposure to H3N2 and H1N1. CONCLUSIONS: Thus, this study identified evidence of seasonality in influenza-like disease in swine herds, but not in circulation of influenza virus. Evidence of seasonality in exposure to influenza was dependent on assumptions of between-month correlation. High exposure to H3N2 and H1N1 subtypes warrants more detailed investigation of within-herd influenza virus circulation. The study provides initial insight into seasonality of influenza in swine and should be followed with herd-level studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".