Spatial and temporal analysis of West Nile virus infection in mosquito and human populations based on arboviral detection testing data
Bibliographic record
Abstract
West Nile virus (WNV) is a mosquito-borne zoonotic flavivirus which often causes asymptomatic infection in humans but may develop into a deadly neuroinvasive disease. In this study, we aimed to investigate variables potentially associated with human WNV infection using human and mosquito WNV surveillance and monitoring datasets, established over 20 years, from 2003 to 2022, across the province of Ontario, Canada. We combined climatic and geographic data, mosquito surveillance data (n = 3010 sites), blood donation arboviral detection testing data in the human population, and demographic and socio-economic data from Canadian population censuses. We hypothesized that spatio-temporal indices related to mosquito vector habitat and phenology, in addition to human demographic and socio-economic factors, were associated with WNV infection in the human population. Our results show that high habitat suitability of the main WNV vector in southerly locations of this region, Cx. pipiens/restuans (IRR = 2.0), and variables related to lower income (IRR = 2.8), and shelter infrastructure spending (IRR = 0.7), were key risk factors associated with WNV infection among blood donors from 2003 to 2022 across Ontario (R2 = 0.67). These results may inform points of entry for practical intervention aimed at reducing risk of mosquito-borne pathogens in Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".