Contribution of Geographic Information Systems (GIS) in the Analysis of Parasitic Diseases: The Example of the Malaria in the City of Bouaké in Côte d’Ivoire
Bibliographic record
Abstract
Malaria is the most widespread parasitic disease in the world and the most deadly parasitosis in tropical regions. Using a Geographic Information System integrating epidemiologic, entomologic, socio-demographic, cartographic, positioning Global Positioning System (GPS) data and information from satellite images, the objective of this study is to identify areas at risk of malaria transmission as well as their determinants. Our results highlighted spaces at malaria risks and allowed to distinguish two major categories of larval lodges, humid shallows and paddy fields in Bouake where the number of Anopheles An. Gambiae was significantly higher ( p <0.001). Moreover, it was also observed an absolute link between the level of parasitic load of the north-west and north-east neighborhoods and level of malaria prevalence ( p = 0.0275).
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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 | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".