Sustainable, integrated control of native vectors: The case of Chagas disease in Central America
Bibliographic record
Abstract
Despite successes in reducing transmission, Chagas disease (American trypanosomiasis) remains the greatest economic burden of any parasitic disease in Latin America afflicting mostly the poor and further contributing to poverty. We review a long-term (2001-2022), integrated Ecohealth approach that addresses sustainable development goals to reduce risk of Chagas transmission by the main native vector in Central America, Triatoma dimidiata, s.l. The basis of the Ecohealth intervention was the identification of the risk factors for house infestation, an understanding of and collaboration with local communities, and genetic and proteomic studies that revealed the epidemiology and mechanisms of the rapid reinfestation seen following insecticide application. We review the development of this approach from a pilot project in two Guatemalan villages, to an expanded initiative across three countries with vastly different ecology, cultures, and municipal organization, and finally development of a multi-institutional, large-scale project to develop a strategy to tackle the remaining hot spots in Central America. This integrated Ecohealth approach resulted in reduced risk of transmission as measured by a sustained decrease in house infestation without further use of insecticides, a reduction in vectors with human blood meals and the Chagas parasite, as well as other health and economic benefits. We discuss lessons learned and how this approach could be applied to other vector-borne diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".