Eastern Equine Encephalitis Virus: High Seroprevalence in Horses from Southern Quebec, Canada, 2012
Bibliographic record
Abstract
Eastern equine encephalitis virus (EEEV) is a highly pathogenic arbovirus that infects humans, horses, and other animals. There has been a significant increase in EEEV activity in southeastern Canada since 2008. Few data are available regarding nonlethal EEEV infections in mammals, and consequently the distribution and pathogenicity spectrum of EEEV infections in these hosts is poorly understood. This cross-sectional study focuses on the evaluation of viral activity in southern Quebec's horses by seroprevalence estimation. A total of 196 horses, 18 months and older, which had never been vaccinated against EEEV and have never traveled outside Canada, were sampled from 92 barns distributed throughout three administrative regions of southern Quebec. Blood samples were taken from each horse and titrated for EEEV antibodies by plaque reduction neutralization test (PRNT). Equine population vaccination coverage was estimated by surveying horse owners and equine practitioners. PRNT results revealed an EEEV seroprevalence up to 8.7%, with 95% confidence limits ranging from 4.4% to 13.0%. Vaccination coverage was estimated to be at least 79%. Our study reveals for the first time in Canada a measure of EEEV seroprevalence in horses. High seroprevalence in unvaccinated animals challenges the perception that EEEV is a highly lethal pathogen in horses. Monitoring high-risk vector-borne infections such as EEEV in animal populations can be an important element of a public health surveillance strategy, population risk assessment and early detection of epidemics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".