A case study of nonlinear programming approach for repeated testing of HIV in a population stratified by subpopulations according to different risks of new infections
Bibliographic record
Abstract
Motivated by the vision of the Joint United Nations Programme on HIV/AIDS that 90% of people living with HIV will be diagnosed by year 2020, we present an optimization framework regarding repeated testing of an infectious disease which is transmitted unevenly in the population. A subset of HIV surveillance data in Canada with detailed and compatible variables is pooled for statistical analysis. The study population is Men having Sex with Men (MSM) in Canada from the pooled data. Estimated parameters regarding the HIV epidemic in the study population show that, across age strata, the number of new infections is distributed differently from the number of people living with HIV. A nonlinear programming algorithm is developed regarding which strata should be considered for repeated testing. Among strata in which repeated testing is considered, the optimal frequency of testing is calculated by stratum to minimize the expected number of tests per year. Scenarios and options that all fulfil the UNAIDS vision are presented. In addition to minimizing the expected number of tests per year, other considerations are also examined such as annual testing in selected strata and the tolerance to imperfect implementation of the testing program with low coverage or uptake rates.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".