Long-term predictions of humoral immunity after two doses of BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex
Bibliographic record
Abstract
Abstract Summary Background The lipid nanoparticle (LNP)-formulated mRNA vaccines are a widely adopted two-dose vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogeneicity of the vaccine, albeit within a limited study time frame. Our aims were to use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data, to project a longer term understanding of humoral immunity as a function of vaccine type, dosage amount, age, and sex. Methods We developed a mathematical model describing the immunization process of LNP-formulated mRNA vaccines, and fit our model to twenty-two clinical humoral and cytokine BNT162b2 or mRNA-1273 human two-dose vaccination data sets. We incorporated multi-dose effects in our model to specify whether the dosage is standard or low-dose. We further specify the age groups 18-55, 56-70, and 70+ in our fits for two-standard doses of mRNA-1273, and sex in our fits for two-standard doses of BNT162b2. We used non-linear mixed effect models to fit to all similar data types (e.g. standard two-dose BNT162b2 or mRNA-1273, or two low-dose mRNA-1273). Therefore, in our fits all estimated parameters are statistically correlated, which allowed us to determine the underlying ‘population-dynamics’ structure common to a data type. We therefore made accurate long-term predictions informed by all clinical data used in this study. Findings We estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals loosing more than 99% humoral immunity relative to peak immunity by eight months following the second dose. We predict that within an eight month period following dose two (corresponding to the CDC time-frame for administration of a third dose), there exists a period of time longer than one month where an individual has less then 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by eight months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the vaccine uptake and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays substantially slower relative to peak immunity gained than compared to two standard doses of either mRNA-1273 or BNT162b2. Interpretation For the two dose mRNA vaccines, our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies. We further show that age plays a critical role in determining the antibody levels. Hence, a third booster dose may confer an immuno-protective advantage in older individuals. Funding This research is supported by NSERC Discovery Grant (RGPIN-2018-04546), NSERC COVID-19 Alliance Grant ALLRP 554923-20, CIHR-Fields COVID Immunity Task Force, NRC Pandemic Response Challenge Program Grant No. PR016-1.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".