A quantitative evaluation of the impact of vaccine roll-out rate and coverage on reducing deaths from COVID-19: a counterfactual study on the impact of the delayed vaccination programme in Iran
Bibliographic record
Abstract
Abstract Vaccination has been a crucial factor in the fight against COVID-19 because of its effectiveness in suppressing virus circulation, lowering the risk of severe disease, and ultimately saving lives. Many countries with an early and rapid distribution of COVID-19 vaccines performed much better in reducing their total number of deaths than those with lower coverage and slower roll-out pace. However, we still do not know how many more deaths could have been averted if countries with slower vaccine roll-outs followed the same rate as countries with earlier and faster distribution of vaccines. Here, we investigated counterfactual scenarios for the number of avertable COVID-19 deaths in a given country based on other countries’ vaccine roll-out rates. As a case study, we compared Iran to eight model countries with similar income brackets and dominant COVID-19 vaccine types. Our analysis revealed that faster roll-outs were associated with higher numbers of averted deaths. While Iran’s percentage of fully vaccinated individuals would have been similar to Bangladesh, Nepal, Sri Lanka, and Turkey under counterfactual roll-out rates, adopting Turkey’s rates could have averted up to 50,000 more deaths, whereas following Bangladesh’s rates could have led to up to 52,800 additional losses of lives in Iran. Notably, a counterfactual scenario based on Argentina’s early but slow roll-out rate resulted in a smaller number of averted deaths in Iran, up to 12,600 more individuals. Following Montenegro’s or Bolivia’s model of faster per capita roll-out rates for Iran could have resulted in more averted deaths in older age groups, particularly during the Alpha and Delta waves, despite their lower overall coverage. Also, following Bahrain’s model as an upper bound benchmark, Iran could have averted 75,300 deaths throughout the pandemic, primarily in the >50 age groups. This study provides insights into future decisions on the management of infectious disease epidemics through vaccination strategies by comparing the relative performance of different countries in terms of their timing, pace, and coverage of vaccination in preventing COVID-19 deaths.
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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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".