Sustaining a Regional Emerging Infectious Disease Research Network: A Trust-Based Approach
Bibliographic record
Abstract
The Asia Partnership on Emerging Infectious Diseases Research (APEIR) was initiated in 2006 to promote regional collaboration in avian influenza research. In 2009, the partnership expanded its scope to include all emerging infectious diseases. APEIR partners include public health and animal researchers, officials and practitioners from Cambodia, China, Lao PDR, Indonesia, Thailand and Vietnam. APEIR has accomplished several major achievements in three key areas of activity: (i) knowledge generation (i.e., through research); (ii) research capacity building (e.g., by developing high-quality research proposals, by planning and conducting joint research projects, by adopting a broader Ecohealth/OneHealth approach); and (iii) policy advocacy (e.g., by disseminating research results to policy makers). This paper describes these achievements, with a focus on the partnership's five major areas of emerging infectious disease research: wild migratory birds, backyard poultry systems, socio-economic impact, policy analysis, and control measures. We highlight two case studies illustrating how the partnership's research results are being used to inform policy. We also highlight lessons learned after five years of working hard to build our partnership and the value added by a multi-country, multi-sectoral, multi-disciplinary research partnership like APEIR.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".