Integrative One Health strategies for the surveillance, control, and prevention of vector-borne and zoonotic diseases: bridging human, animal, and environmental health in a changing world
Bibliographic record
Abstract
Vector-borne and zoonotic diseases (VBZDs) represent over 75% of emerging infections globally and continue to impose severe health and economic burdens, especially in lowand middle-income countries.This review examines integrative One Health strategies for the surveillance, control, and prevention of VBZDs, emphasizing the interconnectedness of human, animal, and environmental health.Using thematic synthesis, evidence from multidisciplinary studies were analyzed to identify effective interventions and persistent challenges.The findings reveal that the environmental, socioeconomic, behavioral, climate change, and cultural factors have contributed to the emergence and global spread of these diseases, rendering public health efforts ineffective with fragmented surveillance systems, weak governance, and inequitable access impeding early outbreak detection and coordinated responses.However, hybrid and digital surveillance models, including artificial intelligencedriven and genomic tools, have demonstrated improved predictive capacity and real-time data sharing.Integrated vector management approaches combining biological, chemical, environmental measures and effective governance mechanisms show enhanced sustainability when coupled with community participation.To protect communities and reduce the burden, we recommend strengthened policy and financing frameworks such as multisectoral coordination committees, pooled One Health budgets, and legally mandated governance, promoting equitable community-driven approaches, incorporating gender-sensitive, participatory, and indigenous knowledge-based approaches.The review highlights that
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".