Mosquito habitat and dengue risk potential in Kenya: alternative methods to traditional risk mapping techniques
Bibliographic record
Abstract
Outbreaks, epidemics and endemic conditions make dengue a disease that has emerged as a major threat in tropical and sub-tropical countries over the past 30 years. Dengue fever creates a growing burden for public health systems and has the potential to affect over 40% of the world population. The problem being investigated is to identify the highest and lowest areas of dengue risk. This paper presents "Similarity Search", a geospatial analysis aimed at identifying these locations within Kenya. Similarity Search develops a risk map by combining environmental susceptibility analysis and geographical information systems, and then compares areas with dengue prevalence to all other locations. Kenya has had outbreaks of dengue during the past 3 years, and we identified areas with the highest susceptibility to dengue infection using bioclimatic variables, elevation and mosquito habitat as input to the model. Comparison of the modelled risk map with the reported dengue epidemic cases obtained from the open source reporting ProMED and Government news reports from 1982-2013 confirmed the high-risk locations that were used as the Similarity Search presence cells. Developing the risk model based upon the bioclimatic variables, elevation and mosquito habitat increased the efficiency and effectiveness of the dengue fever risk mapping process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".