Matching models of HIV-1 viral dynamics to clinical data
Bibliographic record
Abstract
Creating individualized within-host multiple phase disease models of HIV-1 infection has long been a goal of mathematicians and biologists. The challenge is in trying to build models that are representative of the disease, include realistic parameter estimates, and are able to incorporate a changing model structure. In this paper, we propose a fitting procedure, motivated by the biology of the disease, for matching parameters of differential equation and stochastic models of HIV-1 infection to data, which leverages high performance computing resources. The search uses knowledge of the biological set points to restrict the search domain, and parallel simulated annealing to match the model to acute and early chronic phase patient data. We highlight this method by finding parameters for two interconnected models of HIV-1 infection which we have developed. The high quality of our data allows us to model not only viral data, but also CD4 count data through the acute and chronic phases of the disease. The time span of our model exceeds that of previous models. The algorithm is able to find parameter values for four patients consistent with literature ranges and display individual set point equilibration and disease progression for both clinical markers.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".