Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response
Bibliographic record
Abstract
The role of the host immune response in determining the severity and duration of an influenza infection is still unclear. Inorder to identify severity factors and more accurately predict the course of an influenza infection within a human host, anunderstanding of the impact of host factors on the infection process is required. Despite the lack of sufficiently diverseexperimental data describing the time course of the various immune response components, published mathematical models were constructed from limited human or animal data using various strategies and simplifying assumptions. Toassess the validity of these models, we assemble previously published experimental data of the dynamics and role ofcytotoxic T lymphocytes, antibodies, and interferon and determined qualitative key features of their effect that should becaptured by mathematical models. We test these existing models by confronting them with experimental data and find thatno single model agrees completely with the variety of influenza viral kinetics responses observed experimentally whenvarious immune response components are suppressed. Our analysis highlights the strong and weak points of eachmathematical model and highlights areas where additional experimental data could elucidate specific mechanisms,constrain model design, and complete our understanding of the immune response to influenza.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.001 |
| 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".