Predicting age-related determinants of heterogeneous outcomes to COVID-19 mRNA vaccines through mathematical modelling
Bibliographic record
Abstract
ABSTRACT Older adults tend to exhibit weaker vaccine-elicited responses to mRNA COVID-19 immunization than younger people. This is a public health concern, as older individuals are more likely to experience severe COVID-19. To better understand the mechanisms of this age-related disparity, we developed a mathematical model of the post-vaccination humoral immune response. Through calibration to clinical data from 32 healthcare workers (HCWs) and 27 seniors who received the primary vaccine series (two priming doses and one booster), our model predicted that repeated vaccinations consistently enhanced antibody responses in both groups. While seniors were estimated to experience an accelerated decay in T follicular helper cells compared to HCWs, a larger booster dose effectively compensated for this weakened antibody response. Furthermore, we linked antibody and neutralization levels and used this relationship to predict post-vaccination neutralization, thus serving as a proxy for vaccine efficacy. By studying various combinations of mixed doses sizes in the primary vaccination series, our model predicted that administering a full-dose booster significantly enhances immunization outcomes, irrespective of the initial vaccine dose size. Further, a biannual half booster strategy was found to be more effective than one with an annual full booster, especially for seniors. Overall, our findings highlight the importance of tailoring vaccination strategies to different age groups to provide robust and long-lasting immunity against SARS-CoV-2 infections.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".