Planetary Health Design Lab (PHDL): A systems innovation platform to address climate-sensitive zoonoses in Nigeria
Bibliographic record
Abstract
Introduction: Lassa fever, a climate-sensitive zoonotic disease endemic to West Africa, continues to challenge Nigeria’s health system due to environmental disruption, inadequate early warning mechanisms, and fragmented multi-sectoral governance. Between 2019 and 2024, Nigeria recorded over 5,000 confirmed cases, with case fatality ratios ranging from 15% to 20% in high-burden areas. Methods: This concept proposes the Planetary Health Design Lab (PHDL) as a systems innovation platform for integrating ecological and health data to address climate-sensitive zoonoses. The framework is designed to incorporate scenario modeling, spatial prioritization, and participatory systems mapping to co-create context-specific interventions. The proposed approach seeks to adapt decision-support systems for planetary health governance in LMICs. The conceptual model will be piloted using Nigeria as a case study, focusing on Lassa fever, with potential for international adaptation through future collaborations. Results: Early application of the PHDL in Nigeria demonstrates its potential to: (1) strengthen health and environment collaboration, (2) guide anticipatory interventions in Lassa fever hotspots, and (3) connect planetary health research with global technical partners. Cross-country partnerships in Japan and Canada offer complementary decision-support frameworks, including forest sector modeling and cumulative effects tools, adaptable to LMIC contexts. Conclusion: The PHDL offers a transdisciplinary, locally embedded, and globally networked innovation ecosystem for addressing Lassa fever and similar health risks at the human–nature interface. By institutionalizing systems thinking and nature-based governance, the lab supports sustainable epidemic preparedness and planetary health equity.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".