Dengue Fever and Its Burden in Burkina Faso: An Overview
Bibliographic record
Abstract
Dengue fever is an arbovirus disease caused by the dengue virus and has been diagnosed in Burkina Faso for many years. In recent decades, the disease has become a growing concern, thereby impacting the public health system. Several factors contribute to the pathogenesis of dengue fever, including the immune system and the virulence of different serotypes. Additionally, multiple complex conditions, including the spread of the Aedes mosquito vector and meteorological factors, contribute to the disease's spread. Therefore, effective disease management must be comprehensive, involving strategic combinations including community engagement, mosquito control, and public health measures. This approach has been implemented in Burkina Faso, with some success. Although several studies have focused on viral control, the isolation of virus serotypes, the prevalence and seroprevalence of the disease in specific populations, information on the overall burden of dengue fever is scarce in the country, as it presents classic symptoms similar to those of malaria and some arbovirus diseases encountered in the country. However, limited access to diagnostic tools, an inadequate surveillance system, a lack of awareness among healthcare workers, auto-medication, and ongoing conflicts in the country may lead to an underestimation of its burden and a limited understanding of its epidemiology. Here, we discuss dengue fever and the factors associated with the underestimation of its burden in Burkina Faso, drawing on government documents and published data. This review aims to describe the impact of managing this neglected tropical disease, advocating for improved surveillance and control efforts in the country.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".