Individualized Mechanistic Modeling Reveals Viral Infectivity and CD8 <sup>+</sup> T cell Expansion as Drivers of Heterogeneous Influenza Dynamics
Bibliographic record
Abstract
Abstract Influenza virus infections vary widely in severity, with human challenge studies revealing substantial heterogeneity in viral shedding and immune responses. However, the mechanistic basis of this variability remains unresolved. Here, we applied individualized mechanistic modeling to determine how variation in viral replication and CD8 + T cell responses shape infection kinetics and symptom dynamics once variation in exposure dose is removed in individuals experimentally challenged with H1N1 influenza virus. Our analysis identified four distinct infection clusters driven predominantly by individualized differences in viral infectivity and CD8 + T cell expansion rates rather than baseline T cell levels or killing efficacy. These mechanistic distinctions were conserved across independent cohorts and enabled accurate prediction of symptom trajectories despite subjectivity in symptom reporting and differences in viral strain. Collectively, these findings demonstrate that interindividual variation can converge on similar infection trajectories and clinical outcomes and provide a mechanistic basis for improving individual-level prediction of influenza infection dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".