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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".