An Investigation of West Nile Virus Surveillance Activities in Ontario, 2002 - 2008
Bibliographic record
Abstract
This thesis is an investigation of the utility of data on dead corvids found by the public and tested in surveillance for West Nile virus (WNv) in Ontario, Canada. The aim of this thesis is to improve understanding of the predictive ability of dead wild corvids in the surveillance of WNv for human infections. Surveillance data obtained through citizen reports of found dead wild corvids (American Crows, Ravens, Magpies and Jays), some of which were submitted for WNv testing, are examined for their relative time-to-detection and consistency in time and space compared with trapped mosquito and human clinical case data between 2002 - 2008. Phone call reports obtained from citizens across Ontario during 2002 were also explored for their association with sociodemographic factors, and for their timeliness and reliability in WNv detection compared with test-positive corvids. Based on results from survival analysis, the dead corvid surveillance program identified WNv within public health units in Ontario more quickly, and were more predictive of human cases, than mosquito testing during the first few years of surveillance. During the later years of the study period, mosquito testing showed faster time-to-detection. Regional and sociodemographic factors influenced the speed of detection, depending on the surveillance modality. Clusters of early seasonal WNv-positive mosquitoes identified using scan statistics were spatially alike to, but not predictive for clusters of early seasonal human clinical cases. A cluster of early WNv-positive corvids preceded a spatially similar cluster of human cases after influencing geographic and sociodemographic factors were accounted for. Rates of phone call reports of dead corvids were also associated with sociodemographic factors within small areas across the province. Ultimately, the phone calls alone were not as timely or specific in comparison with the testing of corvids in the identification of areas with WNv. The data obtained through the help of citizens in Ontario provided a timely and effective approach to surveillance of a disease that was identifiable through observation of their surrounding environment. However, knowledge of the underlying factors influencing citizen reporting rates is important to allow informed adjustments for cluster detection to reduce confounding bias.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".