The Potential Clinical Impact of Implementing Different COVID-19 Boosters in Fall 2022 in the United States
Bibliographic record
Abstract
Abstract Objective Emerging SARS-COV-2 variants are spurring the development of adapted vaccines as public health authorities plan for the fall vaccination strategy. We aimed to estimate the number of infections and hospitalizations prevented by three potential booster strategies in those ≥18 years of age in the United States: Boosting with Moderna’s licensed first generation monovalent vaccine mRNA-1273 (ancestral strain) starting in September 2022, boosting with Moderna’s candidate bivalent vaccine mRNA-1273.214 (ancestral + BA.1 variant of concern [VOC]) starting in September 2022, or boosting with Moderna’s updated candidate bivalent vaccine mRNA-1273.222 (ancestral + BA.4/5 VOC) starting 2 months later in November 2022 due to longer development timeline. Methods An age-stratified, transmission dynamic, Susceptible-Exposed-Infection-Recovered (SEIR) model, adapted from previous literature, was used to estimate the number of infections over time; the model contains compartments defined by both SEIR status and vaccination status. A decision tree was subsequently used to estimate the clinical consequences of those infections. Calibration was performed so the model tracks the actual course of the pandemic up to the present time. Results Vaccinating with mRNA-1273(Sept), mRNA-1273.214(Sept), and mRNA-1273.222(Nov) is predicted to reduce infections by 34%, 40%, and 18%, respectively, over a 6-month time horizon (September-February) compared to no booster. Similarly, boosting in September prevents substantially more hospitalizations than starting to boost in November with a more effective vaccine (42%, 48%, and 25% for mRNA-1273, mRNA-1273.214, and mRNA-1273.222, respectively, at 6 months compared to no booster). Sensitivity analyses around transmissibility, vaccine coverage, masking, and waning of natural and vaccine-induced immunity changed the magnitude of cases prevented but boosting with mRNA-1273.214 in September consistently prevented more cases of infection and hospitalization than the other two strategies. Limitations and Conclusions With the emergence of new variants, key characteristics of the virus that affect estimates of spread and clinical impact also evolve, making estimation of these parameters difficult, especially in heterogeneous populations. Our analysis demonstrated that vaccinating with the bivalent mRNA-1273.214 booster was more effective over a 6-month period in preventing infections and hospitalizations with a BA.4/5 subvariant than the tailored vaccine, simply because it could be deployed 2 months earlier. We conclude that there is no advantage to delay boosting until a BA.4/5 vaccine is available; earlier boosting with mRNA-1273.214 will prevent the most infections and hospitalizations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".