Gaps and Opportunities for Data Systems and Economics to Support Priority Setting for Climate-Sensitive Infectious Diseases in Sub-Saharan Africa: A Rapid Scoping Review
Bibliographic record
Abstract
Abstract Climate change alters risks associated with climate-sensitive infectious diseases (CSIDs) with pandemic potential. This poses additional threats to already vulnerable populations, further amplified by intersecting social factors, such as gender and socioeconomic status. Currently, critical evidence gaps and inadequate institutional and governance mechanisms impact on the ability for African States to prevent, detect and respond to CSIDs. The aim of this study was to explore the role of data systems and economics to support priority setting for CSID preparedness in sub-Saharan Africa. We conducted a rapid scoping review to identify existing knowledge and gaps relevant to economics and data systems. A literature search was performed across six bibliographic databases in November 2023. A list of 14 target pathogens, identified by the World Health Organization as Public Health Emergencies of International Concern or R&D Blueprint Pathogens, was adopted and compared to a database of CSIDs to determine relevant inclusion criteria. Extracted data were synthesised using bibliometric analysis, thematic topic categorisation, and narrative synthesis to identify research needs, evidence gaps, and opportunities for priority setting. We identified 68 relevant studies. While African author involvement has been increasing since 2010, few studies were led by senior authors from African institutions. Data system studies (n = 50) showed broad coverage across CSIDs and the WHO AFRO region but also a high degree of heterogeneity, indicating a lack of clearly defined standards for data systems related to pandemic preparedness. Economic studies (n = 18) primarily focused on COVID-19 and Ebola and mostly originated from South Africa. Both data system and economic studies identified limited data sharing across sectors and showed a notable absence of gender sensitivity analyses. These significant gaps highlight important opportunities to support priority setting and decision-making for pandemic preparedness, ultimately leading to more equitable health 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 | 0.066 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.038 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".