Exploring factors affecting the reintroduction and amplification of West Nile virus in heterogeneous landscapes in Canada, using a cellular automata approach
Bibliographic record
Abstract
West-Nile virus (WNV) is an endemic public health risk in Canada, with outbreaks/reintroduction and amplification that may increase in frequency and size with climate change and urbanization. In this modeling study, we used a compartmentalized and spatialized Susceptible, Exposed, Infected, Recovered (SEIR) WNV transmission model incorporating a cellular automata approach. We tested four scenarios in which we modified the number of infected birds arriving in spring, modified the number of infected mosquitoes emerging from their overwintering/dormancy period, studied the impact of bird abundance on epidemic starting point locations, and examined the progressive shift in mosquito feeding preferences from birds to mammals. First, we observed that WNV amplification may be associated with the arrival of infected migratory birds in the spring, with more severe epidemics as the number of infected birds increases. Secondly, amplification due to the local persistence of WNV virus in surviving infected overwintering female mosquitoes resulted in more severe epidemics in the human population than when amplification was due to the arrival of infected birds. Thirdly, epidemics were more severe when initiated in cells with low bird density than in those with high density. Lastly, the shift in mosquito feeding preference to human blood meals at the end of summer could generate more cases in human populations if reservoir birds delay their migration and stay longer, amplifying the virus locally. A field study is needed to quantify the impact of these mechanisms on WNV reintroduction in southeastern Canada, to better design interventions and early warning systems.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".