Operationalising Regional Cooperation for Infectious Disease Control: A Scoping Review of Regional Disease Control Bodies and Networks
Bibliographic record
Abstract
The rapid spread of the coronavirus disease 2019 (COVID-19) pandemic demonstrates the value of regional cooperation in infectious disease prevention and control. We explored the literature on regional infectious disease control bodies, to identify lessons, barriers and enablers to inform operationalisation of a regional infectious disease control body or network in southeast Asia.We conducted a scoping review to examine existing literature on regional infectious disease control bodies and networks, and to identify lessons that can be learned that will be useful for operationalisation of a regional infectious disease control body such as the Association of Southeast Asian Nations (ASEAN) Center for Public Health Emergency and Emerging Diseases.Of the 57 articles included, 53 (93%) were in English, with two (3%) in Spanish and one (2%) each in Dutch and French. Most were commentaries or review articles describing programme initiatives. Sixteen (28%) publications focused on organisations in the Asian continent, with 14 (25%) focused on Africa, and 14 (25%) primarily focused on the European region. Key lessons focused on organisational factors, diagnosis and detection, human resources, communication, accreditation, funding, and sustainability. Enablers and constraints were consistent across regions/ organisations. A clear understanding of the regional context, budgets, cultural or language issues, staffing capacity and governmental priorities, is pivotal. An initial workshop inclusive of the various bodies involved in the design, implementation, monitoring or evaluation of programmes is essential. Clear governance structure, with individual responsibilities clear from the beginning, will reduce friction. Secure, long-term funding is also a key aspect of the success of any programme.Operationalisation of regional infectious disease bodies and networks is complicated, but with extensive groundwork, and focus on organisational factors, diagnosis and detection, human resources, communication, accreditation, funding, and sustainability, it is achievable. Ways to promote success are to include as many stakeholders as possible from the beginning, to ensure that context-specific factors are considered, and to encourage employees through capacity building and mentoring, to ensure they feel valued and reduce staff turnover.
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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 | 0.063 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".