Effective surveillance systems for vector-borne diseases in urban settings and translation of the data into action: a scoping review
Bibliographic record
Abstract
BACKGROUND: Vector-borne diseases (VBDs) continue to represent a global threat, with "old" diseases like malaria, and "emergent" or "re-emergent" ones like Zika, because of an increase in international trade, demographic growth, and rapid urbanization. In this era of globalization, surveillance is a key element in controlling VBDs in urban settings, but surveillance alone cannot solve the problem. A review of experiences is of interest to examine other solution elements. The objectives were to assess the different means of VBD surveillance in urban environments, to evaluate their potential for supporting public health actions, and to describe the tools used for public health actions, the constraints they face, and the research and health action gaps to be filled. MAIN BODY: For this scoping review we searched peer-reviewed articles and grey literature published between 2000 and 2016. Various tools were used for data coding and extraction. A quality assessment was done for each study reviewed, and descriptive characteristics and data on implementation process and transferability were analyzed in all studies. After screening 414 full-text articles, we retained a total of 79 articles for review. The main targets of the articles were arboviral diseases (65.8%) and malaria (16.5%). The positive aspects of many studies fit within the framework of integrated vector management. Public awareness is considered a key to successful vector control programs. Advocacy and legislation can reinforce both empowerment and capacity building. These can be achieved by collaboration within the health sector and with other sectors. Research is needed to develop well designed studies and new tools for surveillance and control. CONCLUSIONS: The need for surveillance systems in urban settings in both developing and developed countries was highlighted. Countries face the same challenges relating to human, financial, and structural resources. These findings also constitute a wake-up call for governments, academia, funders, and World Health Organization to strengthen control programs and enhance VBD research in urban environments.
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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 | 0.033 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".