Lives saved and hospitalizations averted by COVID-19 vaccination in New York City: a modeling study
Bibliographic record
Abstract
BACKGROUND: Following the start of COVID-19 vaccination in New York City (NYC), cases have declined over 10-fold from the outbreak peak in January 2020, despite the emergence of highly transmissible variants. We evaluated the impact of NYC's vaccination campaign on saving lives as well as averting hospitalizations and cases. METHODS: We used an age-stratified agent-based model of COVID-19 to include transmission dynamics of Alpha, Gamma, Delta and Iota variants as identified in NYC. The model was calibrated and fitted to reported incidence in NYC, accounting for the relative transmissibility of each variant and vaccination rollout data. We simulated COVID-19 outbreak in NYC under the counterfactual scenario of no vaccination and compared the resulting disease burden with the number of cases, hospitalizations and deaths reported under the actual pace of vaccination. FINDINGS: We found that without vaccination, there would have been a spring-wave of COVID-19 in NYC due to the spread of Alpha and Delta variants. The COVID-19 vaccination campaign in NYC prevented such a wave, and averted 290,467 (95% CrI: 232,551 - 342,664) cases, 48,076 (95% CrI: 42,264 - 53,301) hospitalizations, and 8,508 (95% CrI: 7,374 - 9,543) deaths from December 14, 2020 to July 15, 2021. INTERPRETATION: As the Delta variant sweeps predominantly among unvaccinated individuals, our findings underscore the urgent need to accelerate vaccine uptake and close the vaccination coverage gaps. FUNDING: This study was supported by The Commonwealth Fund.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".