Reducing risk of emerging infectious diseases in Bangladesh through ecohealth
Bibliographic record
Abstract
Background: Landless and ultra-poor inhabitants of the island Chars area of northern Bangladesh face severe food shortages exacerbated by flooding and drought. Livestock management patterns result in exposure to pathogens from livestock waste, increasing the risk of zoonotic and emerging infectious diseases (EIDs). Education and employment opportunities are extremely limited, poverty rates exceed 90%, few women are able to read, and more than 75% of children in the region are below standard height and weight guidelines, primarily due to protein and energy insufficiency. We report on a project that reduces the risk of EIDs by reducing human exposure to zoonotic pathogens of animal origin and by increasing food security. Methods: We collected data representing human and veterinary health, economic, and agricultural production from 1500 villagers in 300 households in Bogra, Jamalpur, and Sirajganj Districts of northern Bangladesh to determine the impact of increased veterinary care of dairy cows, behaviour change to reduce exposure of villagers to manure and other sources of zoonotic disease, and improved agricultural production to increase household income. Pre- and post-intervention data were compared for significant change. Results: Changes contributing to significant reduction of exposure to EID hazards included: removal of livestock from one in three households; improved manure management in all villages; improved water and human waste management through use of latrines in all villages; and increased access to human and veterinary health services for most villages. Average household income increased more than 100% resulting in higher household consumption of purchased protein and energy sources. These results did not capture how villagers understand the concept of adaption to complex systems. Conclusion: This research reflects five of the six pillars of ecohealth (transdisciplinarity, community participation, gender and economic equity, sustainability, and knowledge to action). Risk of emerging infectious disease in poor villages can be reduced through changes in household and village behaviour including removal of livestock from households, increased agricultural production yielding higher household incomes directed at improved food security, and improved delivery of animal and human health services. However, future research needs to address resilience in a vulnerable ecosystem to capture understanding and response to complex adaptive systems.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".