A scoping review of importation and predictive models related to vector-borne diseases, pathogens, reservoirs, or vectors (1999–2016)
Bibliographic record
Abstract
BACKGROUND: As globalization and climate change progress, the expansion and introduction of vector-borne diseases (VBD) from endemic regions to non-endemic regions is expected to occur. Mathematical and statistical models can be useful in predicting when and where these changes in distribution may happen. Our objective was to conduct a scoping review to identify and characterize predictive and importation models related to vector-borne diseases that exist in the global literature. METHODS: A literature search was conducted to identify publications published between 1999 and 2016 from five scientific databases using relevant keywords. All publications had to be in English or French, and include a predictive or importation model on VBDs, pathogens, reservoirs and/or vectors. Relevance screening and data characterization were performed by two reviewers using pretested forms. The data were analyzed using descriptive statistics. RESULTS: The search initially identified 19 710 unique articles, reports, and conference abstracts. This was reduced to 428 relevant documents after relevance screening and data charting. About half of the models used mathematical techniques, and the remainder were statistical. Most of the models were predictive (87%), rather than importation (5%). The most commonly investigated diseases were malaria and dengue fever. Around 12% of the publications did not report all the parameters used in their model. Only 29% of the models incorporated the impacts of climate change. CONCLUSIONS: A wide variety of mathematical and statistical models on vector-borne diseases exist. Researchers creating their own mathematical and/or statistical models may be able to use this scoping review to be informed about the diseases and/or regions, parameters, model types, and methodologies used in published models.
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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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".