Enhancing global health security responses through greater inclusion of the global south in infectious disease modelling
Bibliographic record
Abstract
As we mark 5 years since the emergence of the COVID-19 pandemic, the world continues to face numerous epidemics of emerging and re-emerging diseases, including mpox, cholera, avian influenza, chikungunya and dengue. Concurrently, long-standing diseases such as pneumonia, tuberculosis (TB) and malaria continue to account for millions of premature and preventable deaths globally. Despite the valuable lessons in disease prevention and control during both pandemic and non-pandemic periods, gaps remain in our responses to global public health challenges, including limits to our ability to consolidate, share and analyse data to generate the evidence that policy-makers need to make the best decisions possible, at both local and global levels. Addressing these gaps requires sustained investment, development and retention of modelling and analytical capabilities, and collaboration across jurisdictions, both within and across regions, with the aim of building a united and effective global health response system. Capacity is also strengthened from sharing knowledge about the state of the art in research, and on best practices, and learning in international fora such as conferences. Efforts to enhance modelling capabilities are particularly crucial for resource-constrained countries, many of which are located in the Global South, where the gaps in data collection and analytics training are most pronounced.1
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".