Impact of economic constraints on a Chlamydia trachomatis screening program
Bibliographic record
Abstract
Abstract: Mathematical and computational models are one of several tools which can be employed by policy makers interested in determining the impact of screening on the control of infectious diseases. Current models focus on quantifying prevalence reduction as a result of screening programs; how to best structure a screening program under a limited budget remains an open question. Here we use optimal control theory, a mathematical optimization technique, to investigate how a screening program can be implemented to minimize the economic costs of chlamydia infections when a screening program is in place. Applying this technique to the National Chlamydia Screening Program (NCSP) in the UK, we consider two different but entirely plausible minimization goals which lead to dramatically different screening strategies. Using numerical results, we obtain estimates of optimal yearly screening rates, budget costs, and the expected decrease in chlamydia prevalence. Our methods allow us to estimate the budget needed to fund an optimal screening strategy, to determine how the screening program will change according to desired public health outcomes, and to indicate how to best allocate a pre-determined budget. We conclude by considering the implications of our study to the NCSP and other screening programs.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".