The Ecological, Biological, and Social Determinants of Dengue Epidemiology in Latin America and the Caribbean: A Scoping Review of the Literature
Bibliographic record
Abstract
Dengue has re-emerged in Latin America and the Caribbean (LAC) over the last five decades. The factors influencing dengue transmission by the Aedes aegypti mosquito vector within the region can be classified as ecological, biological, and social determinants. In this review, we summarized the published literature on the evidence for the determinants of dengue vector dynamics, transmission, and epidemiological outcomes in LAC. We searched PubMed, SCOPUS, and LILACS databases in September 2022 to collect published works irrespective of study design published in either English, French, Portuguese, or Spanish. Full-text articles were obtained for the studies that passed the title and abstract screening process. During full-text screening, articles were assessed to determine if they met the eligibility criteria. Data were extracted using NVivo™ 12. Data were organized as NVivo codes. Themes were compiled and communicated narratively. We included 90 peer-reviewed research articles from LAC between 2007 and 2022. The included studies were from 15 different countries, dependencies, and territories in the region. Several dengue-related indicators and outcomes were classified as ecological, biological, or social. Eight main factors were found, including: micro- and macro-climatic factors; entomological and pathogenic factors; and global-, community-, household-, and individual- level social factors. We identified several existing knowledge gaps in the literature. Making salient these gaps may serve as a starting point for future vector-borne infectious disease research to equip policymakers and public health practitioners to develop effective strategies to mitigate the impact of dengue and protect vulnerable populations in LAC.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".