Stochastic Analysis of Pre- and Postexposure Prophylaxis against HIV Infection
Bibliographic record
Abstract
The events that occur following HIV exposure, preceding any detectable infection, are difficult to study experimentally. However, there is considerable evidence that these events can be influenced by the action of antiretroviral drugs, taken either as pre- or postexposure prophylaxis (PrEP and PEP, respectively). We present simple theoretical models of HIV dynamics immediately following exposure, and apply these models to understanding how drug prophylaxis can act to reduce the risk of infection. Because HIV infection following exposure is a relatively rare event, we work with stochastic models which we base on continuous-time branching processes, allowing us to compute the risk of infection under different scenarios. We obtain analytical solutions for viral extinction probabilities, allowing us to avoid extensive computer simulations. We predict in the case of PrEP that reverse transcriptase inhibitors should be somewhat more effective than protease inhibitors and also that single drugs should be nearly as effective as a combination approach. We then model viral dynamics under PEP and find that fast initiation of therapy is essential for risk reduction. However, we predict that a two-week PEP regimen would be nearly as effective as the current recommendation of four weeks of therapy. Our work provides a coherent platform for studying the early dynamics of HIV and indicates possible directions for experimental and theoretical work.
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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 | 0.002 | 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) | 0.003 | 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".