Modelling the impact of a high-uptake bivalent booster scenario on the COVID-19 burden and healthcare costs in New York City
Bibliographic record
Abstract
Background: Uptake of the COVID-19 bivalent booster vaccine (targeting the original SARS-CoV-2 strain and subvariants BA.4 and BA.5 of the Omicron variant) among eligible residents of New York City (NYC) has been modest and declining. Assessing the impact of improved population-level booster coverage with bivalent vaccines in NYC can help inform investment towards vaccination and potential cost-savings. Methods: We calibrated an agent-based model of disease transmission to confirmed and probable cases of COVID-19 in NYC and simulated it to project outcomes under two scenarios. In the base case scenario, we assumed that vaccination continued with the average daily rate of 92 vaccine doses per 100,000 administered during December 2022. In the counterfactual scenario, we modeled a high-uptake scenario between January 1, 2023 and March 31, 2023, with an average daily rate of 296 vaccine doses per 100,000 population that increased bivalent coverage in NYC to match the age-specific influenza vaccine coverage of the 2020-2021 season. Vaccination rate outside the campaign duration remained the same as the base case scenario. Findings: Compared to the base case, the high-uptake scenario averted 88,274 (95% Confidence Interval [CI]: 77,097-100,342) cases, and prevented 2,917 (95% CI: 2,557-3,267) hospitalizations between January 1 through the end of June 2023. Averted outcomes resulted in net savings of $217.2 (95% CI: 190.0-242.2) million in direct healthcare costs. We estimated that the high-uptake scenario would avert 72,879 (95% CI: 63,894-82,228) days of student absenteeism from schools due to COVID-19 illness. Interpretation: Our results illustrate the continued benefits of COVID-19 vaccines in preventing severe health outcomes, averting healthcare costs, and maintaining educational continuity in NYC. Funding: The Canadian Institutes of Health Research, The Natural Sciences and Engineering Research Council of Canada, NIH, Centers for Disease Control and Prevention (CDC), NSF, The Commonwealth Fund, and The Notsew Orm Sands Foundation.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".