Vaccine nationalism and the dynamics and control of SARS-CoV-2
Bibliographic record
Abstract
Vaccines provide powerful tools to mitigate the enormous public health and economic costs that the ongoing SARS-CoV-2 pandemic continues to exert globally, yet vaccine distribution remains unequal between countries. To examine the potential epidemiological and evolutionary impacts of ‘vaccine nationalism’, we extend previous models to include simple scenarios of stockpiling. In general, we find that stockpiling vaccines by countries with high availability leads to large increases in infections in countries with low vaccine availability, the magnitude of which depends on the strength and duration of natural and vaccinal immunity. Additionally, a number of subtleties arise when the populations and transmission rates in each country differ depending on evolutionary assumptions and vaccine availability. Furthermore, the movement of infected individuals between countries combined with the possibility of increases in viral transmissibility may greatly magnify local and combined infection numbers, suggesting that countries with high vaccine availability must invest in surveillance strategies to prevent case importation. Dose-sharing is likely a high-return strategy because equitable allocation brings non-linear benefits and also alleviates costs of surveillance (e.g. border testing, genomic surveillance) in settings where doses are sufficient to maintain cases at low numbers. Across a range of immunological scenarios, we find that vaccine sharing is also a powerful tool to decrease the potential for antigenic evolution, especially if infections after the waning of natural immunity contribute most to evolutionary potential. Overall, our results stress the importance of equitable global vaccine distribution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".