Predicting Weekly Variation of<i>Culex tarsalis</i>(Diptera: Culicidae) West Nile Virus Infection in a Newly Endemic Region, the Canadian Prairies
Bibliographic record
Abstract
West Nile virus (WNV) spread across most of North America within a short time period after its incursion into the Western Hemisphere. The Canadian prairies had the highest human incidence of WNV disease in Canada, particularly in 2007. Statistical modeling and geographic information systems can be used to develop a predictive model and facilitate the mobilization of targeted disease management strategies. Using data collected between 2005 and 2008, we constructed models integrating abiotic and biotic factors to predict the WNV infection rate in female Culex tarsalis Coquillett, the primary vector of WNV in the Canadian prairies. During the study period, the highest mean Cx. tarsalis infection rate was during week 34 (late August). The Cx. tarsalis infection rate increased with increasing Cx. tarsalis abundance and mean temperature lagged from 1 to 8 wk, but decreased with increasing mean precipitation lagged from 2 to 6 wk. Furthermore, precipitation was a 'distorter variable' that altered the association between Cx. tarsalis abundance and the WNV infection rate. Our model clarified how weather influenced the Cx. tarsalis infection rate in the Canadian prairies, a newly and highly WNV endemic region of North America. An understanding of the role of lagged weather variables was essential for providing sufficient lead time to predict WNV occurrence, and for implementing disease control and prevention strategies. Furthermore, it is a useful tool for assessing the potential effects of future climate change on WNV in areas near its northern distributional limit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".