Screen more or screen more often? Using mathematical models to inform syphilis control strategies
Bibliographic record
Abstract
BACKGROUND: Syphilis incidence among men who have sex with men (MSM) continues to rise despite attempts to increase screening and treatment uptake. We examined the marginal effect of increased frequency versus increased coverage of screening on syphilis incidence in Toronto, Canada. METHODS: We developed an agent-based, network model of syphilis transmission, representing a core population of 2,000 high-risk MSM. Epidemiological and biological parameters were drawn from regional surveillance data and literature-derived estimates. The pre-intervention period of the model was calibrated using surveillance data to identify 1000 credible simulations per strategy. Evaluated strategies included: annual syphilis screening at baseline coverage, increased screening frequency at baseline coverage, and increased coverage of annual screening. Intervention impact was measured as annual prevalence of detected infectious cases and syphilis incidence per year over 10 years. RESULTS: Of the strategies evaluated, increasing the frequency of syphilis screening to every three months was most effective in reducing reported and incident syphilis infections. Increasing the fraction of individuals tested, without increasing test frequency, resulted a smaller decline in incidence, because reductions in infectious syphilis via treatment were counterbalanced by increased incident syphilis among individuals with prior latent syphilis. For an equivalent number of additional tests performed annually, increased test frequency was consistently more effective than improved coverage. CONCLUSIONS: Strategies that focus on higher frequency of testing in smaller fractions of the population were more effective in reducing syphilis incidence in a simulated MSM population. The findings highlight how treatment-induced loss of immunity can create unexpected results in screening-based control strategies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".