What Can We Learn From Others to Develop a Regional Centre for Infectious Diseases in ASEAN? Comment on "Operationalising Regional Cooperation for Infectious Disease Control: A Scoping Review of Regional Disease Control Bodies and Networks"
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has brought the need for regional collaboration on disease prevention and control to the fore. The review by Durrance-Bagale et al offers insights on the enablers, barriers and lessons learned from the experience of various regional initiatives. Translating these lessons into action, however, remains a challenge. The Association of Southeast Asian Nations (ASEAN) planned to establish a regional centre for disease control; however, many factors have slowed the realisation of these efforts. Going forward, regional initiatives should be able to address the complexity of emerging infectious diseases through a One Health approach, assess the social and economic impact of diseases on the region and study the real-world effectiveness of regional collaborations. The initiatives should seek to be inclusive of stakeholders including those from the private sector and should identify innovative measures for financing. This advancement will enable regions such as ASEAN to effectively prepare for the next pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.018 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".