Exploring Neglected Tropical Diseases in Somalia: A Scoping Review of Research Efforts and Gaps
Bibliographic record
Abstract
Abstract Neglected tropical diseases (NTDs) comprise 20 chronic and debilitating conditions that affect over 1.7 billion people worldwide, predominantly in marginalized and impoverished communities. In Somalia, the prevalence of NTDs is fueled by limited access to clean water, inadequate sanitation, and insufficient healthcare infrastructure, which disproportionately affect vulnerable groups such as women, children, and rural populations. Prolonged political instability has further impeded efforts to conduct research and establish effective surveillance systems for NTDs. Unlike its neighboring countries, Somalia lacks a comprehensive master plan for NTD prioritization. This scoping review mapped the existing research on NTDs in Somalia, identified knowledge gaps, and proposed future research and policy priorities. We included 36 studies published between January 1968 and July 2025, which reported eight neglected tropical diseases, with visceral leishmaniasis (38.9%) and schistosomiasis (33.4%) being the most studied. Other diseases, such as soil-transmitted helminthiases, chikungunya, rabies, and mycetoma, remain severely under-researched. Despite being a WHO priority country for leprosy, only two studies on this disease were identified. The studies predominantly used descriptive designs, with 52.8% led by authors outside Somalia, highlighting gaps in local research capacity. The review underscores the urgent need for systematic epidemiological studies, enhanced surveillance systems, and integration of NTD research into Somalia’s health policies. Addressing these gaps requires building local research infrastructure, promoting community-based interventions, and fostering collaborations between the government, Somali researchers, and international organizations. This evidence-based approach is vital to mitigating the burden of NTDs and improving health outcomes for Somalia’s underserved populations.
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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 | 0.029 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.036 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".