Multi-species Evaluation of Human Risk of West Nile Virus Infection, Saskatchewan 2003
Bibliographic record
Abstract
In an effort to monitor WNV across Saskatchewan, the provincial health authority and collaborators established an integrated surveillance system for WNV. Surveillance included both human and animal components, as well as environmental variables including precipitation, growing degree days (GDDs), ecozone and landcover. Post-season analysis of the integrated surveillance system included investigating which surveillance variables were successful in predicting human risk of WNV infection in the same areas, determining if clinical horse case data could be used to predict future human cases of WNV, and establishing if early-season seroconversion in sentinel horse herds could predict occurrence of human cases in the same areas. Cluster analysis revealed significant clusters of human and horse cases of WNV in space, time, and space-time; however, only space-time clusters of horse cases preceded human cases by one week in the same area. Significant spatial clusters of human and horse cases did not occur in the same areas. Both human and horse cases clustered during the same time period under temporal analysis. Spatial regression analyses were used to determine which components of surveillance predicted increasing risk of disease in humans and horses in the same regions where surveillance variables were collected. Environmental variables predictive of increasing risk of human cases of WNV included increasing maximum ODDs. Maximum precipitation had a protective effect for both human and horse cases. Both Culex restuans and Culex tarsalis mosquitoes were predictive of increasing risk of human and horse disease. Increasing percentages of positive corvid birds were also predictive of increasing percentages of human cases. The results of these analyses can be used to guide future years' surveillance efforts for WNV in Saskatchewan. Early-season serological surveillance of sentinel horse herds by detection of IgG antibodies using an ELISA test was predictive of human cases of WNV in the same regions. Detection of IgM antibodies in the same samples was not predictive of human risk. Seroconversion of sentinel horse herds was predicted by decreasing maximum precipitation, increasing percentages of Culex restuans and Culex tarsalis, and increasing percentage of the rural municipality covered by water. Early-season serological horse surveillance is an important surveillance technique to predict human risk of WNV infection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".