Impact of economic constraints on a Chlamydia trachomatis screening program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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