Mathematical Model for the Intracellular Kinetics of Influenza a Virus Replication in Vitro
Bibliographic record
Abstract
In the last twenty years, mathematical modelling (MM) has been notably used to capture the infection kinetics of many infectious diseases as it allows insights into the basic biology, infection kinetics, and the mechanisms and efficacy of treatment modalities. MMs of influenza A virus (IAV) infection usually represent the process of virus replication within a cell as a ‘black box’ term for viral production. The simplification is appropriate when we are not interested in the microscopic details of infection. Recently though, MMs have begun to account for the kinetics of intracellular IAV replication. Herein, we examine the MM by Heldt et al., which is able to capture kinetics of IAV infection. It however, does so by adjusting parameters of the MM to various events in the infection process. We developed a robust, yet concise, MM for the intracellular kinetics of influenza A virus infection in vitro with a consistent set of parameters. We use attachment, fusion and RNA data gathered from literature sources to validate our simplified MM and match known infection kinetics consistent throughout infection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".