Moving from reactive response to proactive prevention of emerging infectious diseases: Socio-ecological systems mapping in the Democratic Republic of the Congo
Bibliographic record
Abstract
Emerging infectious diseases such as Ebola and Mpox pose significant public health challenges in the Democratic Republic of the Congo (DRC). Effective prevention policies require a clear understanding of the socio-ecological systems (SES) in which these diseases emerge. This study examined the SES influencing emerging infectious disease prevention in the DRC through five participatory modelling workshops conducted at national, provincial, and community levels using causal loop diagrams (CLDs). Participants were selected through stakeholder analysis to ensure cross-sectoral representation. A structured process guided the co-creation of integrated system maps, beginning with disease-specific models and culminating in validated shared maps. A total of 162 stakeholders participated across the workshops, most of whom were affiliated with government institutions (83%), with smaller proportions from civil society, academia, and technical assistance organizations. The Agriculture and Animal Health sector represented 36% of participants, followed by Human Health (31%) and Environmental Health (13%). Most participants had over 10 years of experience. Analysis of the CLDs revealed that while the number of infected individuals remained the central driver triggering feedback responses, the mechanisms of influence differed by governance level. National and provincial systems were shaped by public investment in One Health systems, political commitment, and governance capacity, whereas community-level dynamics were dominated by socio-economic conditions, hunting practices, and local sensitization. Overall, the findings highlight that current governance remains largely reactive, emphasizing response over prevention. Strengthening One Health governance will require a shift toward proactive health promotion supported by institutionalized coordination, sustained investment, and inclusive community engagement.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".