Developing an eco-bio-social conceptual framework for dengue virus transmission in Latin America and the Caribbean: An e-Delphi study
Bibliographic record
Abstract
Dengue is a rapidly proliferating, mosquito-borne arboviral disease caused by the dengue virus (DENV), and is endemic in Latin America and the Caribbean (LAC). Evidence from the literature suggests that there are several ecological, biological, and social (eco-bio-social) factors determining dengue endemicity in the region. The primary objective of this study was to develop an eco-bio-social conceptual framework for dengue transmission in LAC. The secondary objective was to establish research, and policy and program agendas for dengue prevention and control in LAC. We conducted a sequential, multi-method study using a 3-round e-Delphi method between May and November 2023. Questionnaires were written in English and translated into Spanish and Portuguese, and data were analyzed quasi-anonymously. Professional panelists evaluated the framework using a 10-item, 7-point Likert scale. Consensus was defined as 70% or more agreement among panelists. Panelists also developed a research agenda and evaluated a policy and program agenda using a 12-item, 7-point Likert scale. Eleven dengue professionals from seven LAC countries participated in this study. The most relevant eco-bio-social factors determining dengue transmission were seasonal weather and urban microclimatic oscillations, vectorial capacity and competence, and urbanization and land use. After three consultation rounds, consensus was achieved on a framework comprising 16 ecological, 11 biological, and 28 social factors. Panelists developed a research agenda based on 3 research themes: ecological and environmental; biological and immunological; and social and cultural research. Panelists developed a policy and program agenda for dengue prevention and control, including 4 categories: government investments, integrated programs, intersectoral approaches, and innovative practices. Majority of panelists (88%) agreed that the agenda can improve dengue prevention and control in LAC. The consensus-based eco-bio-social framework and agendas offer novel opportunities to transform dengue prevention and control strategies in LAC and to address the specific needs and experiences of community members in LAC.
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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 | 0.070 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".