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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it