Gaps in the global health research landscape for mpox: an analysis of research activities and existing evidence
Bibliographic record
Abstract
BACKGROUND: Since December 2022, human cases of mpox in the Democratic Republic of the Congo (DRC) have risen at unprecedented rates. We identified a need for systematic mapping of the active research landscape and evidence, assessing their alignment with both local and global research priorities, to inform urgently needed research investments to support response efforts. METHODS: We conducted a mapping review of global research funding and international clinical trial registries and established a systematic rapid research needs appraisals platform to identify existing evidence gaps on mpox research. We analysed the alignment of these to established research categories and both local and globally identified mpox-specific research priorities. RESULTS: We identified 124 ongoing mpox research grants, 79 registered trials and 415 published studies. Most grants (85.0%, n = 105/124), clinical trials (49.3%, n = 39/79) and primary studies (57.7%, n = 205/355) were conducted in high-income countries, with most evidence published in response to the 2022 clade II global mpox outbreaks. Research funding has been focussed on vaccine and therapeutic pre-clinical development. Key gaps remain in both ongoing research and evidence relating to clinical characterisation among populations at risk, clinical trials on effective medical countermeasures specific to clade I, social sciences, health systems research, and healthcare and community protection. CONCLUSIONS: Our findings highlight persistent research gaps related to mpox clade Ib, particularly the limited knowledge on its characteristics and a lack of ongoing efforts to develop effective medical countermeasures, posing a risk to control efforts. Aligning research and investments to locally and globally identified research priorities and evidence gaps will help support national, regional and international responses to prevent transmission and improve outcomes.
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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 | 0.215 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.037 | 0.038 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".