Herd-level prevalence and incidence of porcine epidemic diarrhoea virus (PEDV) and porcine deltacoronavirus (PDCoV) in swine herds in Ontario, Canada
Bibliographic record
Abstract
Porcine epidemic diarrhoea virus (PEDV) and porcine deltacoronavirus (PDCoV) were first identified in Canada in 2014. Surveillance efforts have been instrumental in controlling both diseases. In this study, we provide an overview of surveillance components for the two diseases in Ontario (Canada), as well as PEDV and PDCoV incidence and prevalence measures. Swine herds located in the Province of Ontario, of any type, whose owners agreed to participate in a voluntary industry-led disease control programme (DCP) and with associated diagnostic or epidemiological information about the two swine coronaviruses, were eligible to be included for calculation of disease frequency at the provincial level. PEDV and PDCoV data stored in the industry DCP database were imported into the R statistical software and analysed to produce weekly frequency of incidence counts and prevalence counts, in addition to yearly herd-level incidence risk and prevalence between 2014 and 2016. The yearly herd-level incidence risk of PEDV, based on industry data, was 13.5%, 3.0% and 1.4% (95% CI: 11.1-16.2, 2.0-4.2, 0.8-2.3), while the yearly herd-level incidence risk of PDCoV was 1.1%, 0.3%, and 0.1% (95% CI: 0.5-2.2, 0.1-0.9, 0.0-0.5), for 2014, 2015 and 2016, respectively. Herd-level prevalence estimates for PEDV in the last week of 2014, 2015 and 2016 were 4.4%, 2.3% and 1.4%, respectively (95% CI: 3.1-6.0, 1.5-3.3, 0.8-2.2), while herd-level prevalence estimates for PDCoV in the last week of 2014, 2015 and 2016 were 0.5%, 0.2% and 0.2%, respectively (95% CI: 0.1-1.2, 0.0-0.6, 0.0-0.6). Collectively, our results point to low and decreasing incidence risk and prevalence for PEDV and PDCoV in Ontario, making both diseases possible candidates for disease elimination at the provincial level.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".