Agent-based modelling of West Nile virus propagation in southern Manitoba, Canada
Bibliographic record
Abstract
This research addresses the design and development of a data-driven Agent-Based Modelling (ABM) framework to simulate transmission and spread of West Nile Virus (WNV) among heterogeneous mobile humans, various bird species, and Culex genus mosquitoes over a geographic region at a province-scale. A diverse variety of topics and techniques regarding the data collection phase is presented, as modelling WNV has many disparate attributes. WNV is a mosquito-borne disease influenced by avian species as their amplifying hosts. A significant amount of data, such as home range, flight speed, WNV competence index, etc., regarding over 150 bird species along with their population estimates and locations in Manitoba, Canada are estimated and assembled. The primary contribution of this thesis is the development and validation of a data-driven Cellular Difference Equation (CDiffE) scheme for adoption in WNV-ABMs or other mosquito-borne disease ABMs. The migration patterns of different bird species, nocturnal biting activities of Culex mosquitoes, daily temperature and rainfall, and land cover impact are incorporated into the CDiffE model. The CDiffE model at its core employs difference equations, which are computationally faster than commonly used differential equation models. The proposed CDiffE model is cellular to capture heterogeneity of various geographical areas. The CDiffE has been rigorously verified and validated. While the whole system is designed from an ABM perspective at a cellular level, it exhibits biologically compatible behaviour at the macro-level scale. The proposed CDiffE demonstrates high accuracy in predicting real-world mosquito population trends and geographical distributions, evidenced by the mosquito trap data from Manitoba. The proposed CDiffE model updates on an hourly step to act as an environment for a comprehensive ABM of WNV spread among peripatetic humans. Such a hybrid ABM is successfully built on top of the proposed CDiffE scheme to study the impact of human movements on the prevalence of the virus. The human movement component is modeled on data available from cell phone trajectories as well as census and demographic datasets. Simulation results clearly illustrate the importance of human movement patterns and demonstrate the need for real-world data. Yet human mobility is often disregarded within current WNV modeling efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".