Projections of the incidence of COVID-19 in Japan and the potential impact of a Fall 2023 COVID-19 vaccine
Bibliographic record
Abstract
BACKGROUND: The study objective was to estimate the incidence of COVID-19 infection, hospitalization, and deaths in Japan from September 2023 to August 2024 and potential impact of a monovalent XBB.1.5 variant-adapted Fall 2023 COVID-19 vaccine (modified version: XBB monovalent) for adults aged ≥18 years on these outcomes. METHODS: A previously developed Susceptible-Exposed-Infected-Recovered model for the United States (US) was adapted to Japan. The numbers of symptomatic infections, COVID-19-related hospitalizations, and deaths were calculated. Given differences in vaccination coverage, masking practices and social mixing patterns between the US and Japan, all inputs were updated to reflect the Japanese context. Vaccine effectiveness (VE) values are hypothetical, but predicted based on existing VE values of bivalent BA.4/BA.5 boosters against BA.4/BA.5 in Japan, from the VERSUS test-negative case-control study. Sensitivity analyses were performed. RESULTS: The base case model predicts overall that there will be approximately 35.2 million symptomatic COVID-19 infections, 690,000 hospitalizations, and 62,000 deaths in Japan between September 2023 and August 2024. If an updated COVID-19 vaccine is offered to all adults aged 18 years and older in Fall 2023, the model predicts that 7.3 million infections, 275,000 hospitalizations and 26,000 deaths will be prevented. If vaccines are only given to those aged 65 years and older, only 2.9 million infections, 180,000 hospitalizations and 19,000 deaths will be prevented. Sensitivity analysis results suggest that hospitalizations and deaths prevented are most sensitive to initial VE against infection and hospitalizations, and the waning rate associated with VE against infection. Symptomatic infections prevented was most sensitive to initial VE against infection and VE waning. CONCLUSIONS: Results suggest that a Fall 2023 COVID-19 vaccine would reduce total numbers of COVID-19-related infections, hospitalizations, and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".