Spatial Evaluation of Dengue Transmission and Vector Abundance in the City of Dhaka, Bangladesh
Bibliographic record
Abstract
In recent years, many urban areas in low and middle income countries have experienced major dengue epidemics, and the city of Dhaka, the capital city of Bangladesh, is one of them. Understanding models based on land cover and land use in urban areas in relation to vector abundance and possible disease transmission can be a major epidemiological tool in identifying disease incidence and prevalence. Demographic and human behavioral factors can also play a role in determining microenvironments for entomological distribution—which is a major risk factor for epidemicity. Data collected from a cross-sectional entomological survey in the city of Dhaka during the monsoon season of 2012 and two serological surveys—one pre-monsoon and another post-monsoon in 2012—were analyzed in this study. A total of 898 households and 1003 containers with water were inspected, and 1380 Ae. aegypti pupae and 4174 larvae were counted in these containers. All Stegomyia indices were found to be the highest in the central business and residential mixed zone. The odds ratios of risk factors for seroprevalence, including sex, age, self-reported febrile illness during the previous six months, and travel during the last six months, were calculated; age distribution was found to be a highly significant risk factor (p = value < 0.0001). The study offers clear patterns of dengue viral transmission, disease dynamics, and their association with critical spatial dimensions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".