Modeling West Nile Virus transmission in birds and humans: Advantages of using a cellular automata approach
Bibliographic record
Abstract
In Canada, the periodic circulation of West Nile Virus (WNV) is difficult to predict and, beyond climatic factors, appears to be related to the migratory movements of infected birds from the southern United States. This hypothesis has not yet been explored in a spatially distributed model. The main objective of this work was to develop a spatially explicit dynamic model for the transmission of West Nile virus in Canada, that allows us to explore non-climate related hypotheses associated with WNV transmission. A Cellular Automata (CA) approach for multiple hosts (birds and humans) is used for a test region in eastern Ontario, Canada. The tool is designed to explore the role of host and vector spatial heterogeneity, host migration, and vector feeding preferences. We developed a spatialized compartmental SEIRDS-SEI model for WNV transmission with a study region divided into 4 km2 rectangular cells. We used 2010–2021 bird data from the eBird project and 2010–2019 mosquito data collected by Ontario Public Health to mimic bird and mosquito seasonal variation. We considered heterogeneous bird densities (high and low suitability areas) and homogeneous mosquito and human densities. In high suitability areas for birds, we identified 5 entry points for WNV-infected birds. We compared our simulations with pools of WNV-infected field collected mosquitoes. Simulations and sensitivity analyses were performed using MATLAB software. The results showed good correspondence between simulated and observed epidemics, supporting the validity of our model assumptions and calibration. Sensitivity analysis showed that a 5% increase or decrease in each parameter of our model except for the biting rate of bird by mosquito (c(B,M)) and mosquito natural mortality rate (dM)), had a very limited effect on the total number of cases (newly infected birds and humans), prevalence peak, or date of occurrence. We demonstrate the utility of the CA approach for studying WNV transmission in a heterogeneous landscape with multiple hosts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".