Information to Act: Household Characteristics are Predictors of Domestic Infestation with the Chagas Vector Triatoma dimidiata in Central America
Why this work is in the frame
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Bibliographic record
Abstract
The interruption of vectorial transmission of Chagas disease by Triatoma dimidiata in central America is a public health challenge that cannot be resolved by insecticide application alone. In this study, we collected information on previously known household risk factors for infestation in 11 villages and more than 2,000 houses in Guatemala, Honduras, and El Salvador, and we constructed multivariate models and used multimodel inference to evaluate their importance as predictors of infestation in the region. The models had moderate ability to predict infested houses (sensitivity, 0.32-0.54) and excellent ability to predict noninfested houses (specificity higher than 0.90). Predictive ability was improved by including random village effects and presence of signs of infestation (insect feces, eggs, and exuviae) as fixed effects. Multimodel inference results varied depending on factors included, but house wall materials (adobe, bajareque, and palopique) and signs of infestation were among the most important predictive factors. Reduced models were not supported suggesting that all factors contributed to predictions. Previous knowledge and information from this study show that we have evidence to prioritize rural households for improvement to prevent house infestation with Triatoma dimidiata in Central America. House improvement will most likely have other health co-benefits.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it