Incorporating Drug Adherence Stochasticity Into Pharmacokinetics and Pharmacodynamics to Understand the HIV Transmission Dynamics Through a MultiScale System
Bibliographic record
Abstract
Abstract. HIV patients experience a viral rebound when treatment is interrupted due to poor drug adherence. Lack of adherence is a critical factor to the high transmission and prevalence in the population. In this paper, we propose a multiscale stochastic model that integrated both within-host and between-host dynamics to explore the impact of individual drug adherence on HIV spread, particularly focusing on how variations in adherence affect CD4[Formula: see text] T cell count and viral load at the individual level, as well as transmission rate and disease-induced death rate at the population level. The analysis of the within-host subsystem and the coupled between-host subsystem is presented, including approximation of the steady-state distribution and detailed forms of random attractors. Based on the HIV surveillance data in China, the approximate Bayesian computation method using sequential Monte Carlo is used for data fitting and our fitting results demonstrate that the proposed random model effectively captures the transmission dynamics, especially the fluctuations and slight upward trend year by year. In addition, we perform a scenario analysis with respect to different viral load feedback patterns on drug adherence and investigate how these feedback patterns affect the number of new HIV cases and deaths. Along with a sensitivity analysis for drug adherence parameters, we conclude that positive feedback is more effective in controlling the infection scale for populations with poor adherence. The proposed method provides new insights of the factors that affect prevalence and incidence at the population due to poor drug adherence at the individual level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".