Eco-bio-social determinants of Aedes infestation in Dhaka, Bangladesh
Bibliographic record
Abstract
Background: Globally, vector borne diseases are becoming a significant public health problem, with a number of ‘old’ diseases resurging in recent years alongside newly emerging infectious diseases. Among them, Dengue has become most prominent example. While dengue is regarded as one of the most alarming infectious diseases, its resurgence reflects the failure of traditional reductionistic disciplinary approach in understanding dengue disease transmission process as well as in eliminating and controlling dengue vectors (i.e., Aedesaegypti and Aedesalbopictus). My research is based on the notion that the understanding of the dengue transmission requires the development of a holistic epistemology that can assess the eco-bio-social determinants and their interactions with human action and vice versa. The proposed study has four components: i) determination of dengue virus prevalence, ii)determination of vector density and its correlation with dengue prevalence; iii) effects of local-level social-ecological and human behavioural factors on vector density; and iv) enhancement oflocal community capacity for public participation in health intervention and development policyforums. Methods: The proposed research has adopted a transdisciplinary approach as the basis forunderstanding dengue transmission in Bangladesh and for identifying community-centered interventions.In order to attain the objectives of the research, a total of 842 households from 12 urban wards were surveyed with a specific survey instrument. Vector distribution was monitored and vector density has been calculated by the commonly used larvalindices and the human-hour catch and per room collection of adult vector population. For in-depth understanding and identification of potential interventions, Focus Group Discussions were held in three selected wards of the City of Dhaka. These were supplemented by semi-structured interview of 30 stakeholders representatives; responses from 300 ward/community members; 12 policy- and/or decision makers (national and local institutions), and Mental Map construction of 24 ward representatives (supplemented by 300 ward members). Results: Overall, the findings have revealed that vast majority of the community members are well aware of Aedes infestation, however, very few have taken specific measures to control them in their household and in the neighbourhood. Conclusion: It is suggested that more community ownership will be required to make Aedes control a success.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".