Pandemic preparedness and response: beyond the Access to COVID-19 Tools Accelerator
Bibliographic record
Abstract
Nationalism has trumped solidarity, resulting in unnecessary loss of life and inequitable access to vaccines and therapeutics. Existing intellectual property (IP) regimens, trade secrets and data rights, under which pharmaceutical firms operate, have also posed obstacles to increasing manufacturing capacity, and ensuring adequate supply, affordable pricing, and equitable access to COVID-19 vaccines and other health products in low-income and middle- income countries. We propose: (1) Implementing alternative incentive and funding mechanisms to develop new scientific innovations to address infectious diseases with pandemic potential; (2) Voluntary and involuntary initiatives to overcome IP barriers including pooling IP, sharing data and vesting licences for resulting products in a globally agreed entity; (3) Transparent and accountable collective procurement to enable equitable distribution; (4) Investments in regionally distributed research and development (R&D) capacity and manufacturing, basic health systems to expand equitable access to essential health technologies, and non-discriminatory national distribution; (5) Commitment to strengthen national (and regional) initiatives in the areas of health system development, health research, drug and vaccine manufacturing and regulatory oversight and (6) Good governance of the pandemic prevention, preparedness and response accord. It is important to articulate principles for deals that include reasonable access conditions and transparency in negotiations. We argue for an equitable, transparent, accountable new global agreement to provide rewards for R&D but only on the condition that pharmaceutical companies share the IP rights necessary to produce and distribute them globally. Moreover, if countries commit to collective procurement and fair pricing of resulting products, we argue that we can greatly improve our ability to prepare for and respond to pandemic threats.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".