The TEAIV model: Extending the standard TEIV model to account for viral budding ramp up
Bibliographic record
Abstract
Abstract Understanding how viruses replicate and spread within a host is fundamental to predicting disease progression, timing, and dosage of effective therapeutic and prophylactic interventions. A target-cell limited approach is often used to model within-host viral kinetics to characterize disease infection dynamics. The standard target-cell limited model, the TEIV model, has been instrumental for understanding SARS-CoV-2 within-host viral kinetics, however, its core assumptions of instantaneous viral budding and exponentially distributed cell lifetimes oversimplify fundamental biological processes, potentially limiting predictive power. In this work, we consider a novel model approach, the TEAIV model: an extension to the TEIV model that considers an infected partially-productive cell state that ramps up through n stages to a fully productive state. We fit the TEAIV model to various SARS-CoV-2 viral load datasets, separated by hospitalized (severe infection) and non-hospitalized (mild infection) individual data, consider multiple different ramping functions, and compare to standard TEIV model fits. We further compare TEAIV and TEIV model fits with and without infected cell death incorporated in the eclipse (E) and partially infected (A) compartments and investigate best-fit frameworks up to 10 A states. We find that linear budding ramp-up dynamics minimizes the BIC (with ΔBIC > 2) across all model formulations when the total number of eclipse and budding compartments exceeds three. Furthermore, we find that the inclusion or exclusion of cell death applied to eclipse or ramping compartments does not substantially affect this result. Finally, we analytically consider the most general TEAIV model and discuss future modelling considerations. Our results demonstrate that accounting for non-instantaneous viral budding provides a better fit to SARS-CoV-2 viral load data and establishes a foundation for more mechanistically informed within-host models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".