Bridging Urban Planning and Public Health: Investigating the Relationship Between Land Use Change and Vector-Borne Disease Risks in Ontario
Bibliographic record
Abstract
Zoonotic and vector-borne diseases are diseases that are transmitted from animals to humans, and incidences of zoonotic spillover are on the rise globally due to several anthropogenic factors which have intensified the animal-human interface in recent decades; reshaping reservoir host communities and increasing the novel interactions between people and wildlife. Urbanization and anthropogenic land use change has been identified as an important driver in this phenomenon, and several papers and reports have been published which call on urban planners to help mitigate zoonotic and vector-borne disease risks by safeguarding the planet’s natural resources and ensuring environmentally and socially responsible development practices. The aim of this report was to explore the ways in which Ontario planners can address this global challenge. A scan of data published by Public Health Ontario identified Lyme disease and West Nile Virus (WNV) as the most prevalent zoonotic/vector-borne diseases of public health significance which involve spillover that is impacted by land use and environmental factors. Then, a scoping literature review of eighty-five peer-reviewed articles and reports from reliable organizations was conducted to derive a thematic summary of the land use drivers of Lyme disease and WNV enzootic spillover. The themes were then used to guide semi-structured interviews with public health and planning experts. The results generated approximately fifty recommendations for planners and policy-makers regarding the ways in which the planning frameworks in Ontario could address the issue of vector-borne disease risks. This study serves as a preliminary step in bridging urban planning and public health towards a multi-target goal of fostering healthier, sustainable communities, from a vector-borne disease perspective.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".